void voigtian(RooDataSet *dataset, RooDataSet *dataset2, RooRealVar &variable, RooPlot *fitFrame, RooBinning b, double rangeMin, double rangeMax, vector <double> &fitParameters)
{
RooRealVar mean("mean","mean",0.0,-0.1,0.1);
RooRealVar sigma("sigma","sigma",0.5,0.0,1.0);
RooRealVar width("width","width",0.5,0.0,1.0);
RooVoigtian * pdf = new RooVoigtian("pdf","Voigtian",variable,mean,sigma,width);
dataset->plotOn(fitFrame,Name("myhist"),Binning(b),DataError(RooAbsData::SumW2));
RooFitResult * res = pdf->fitTo(*dataset, Range(rangeMin, rangeMax),Save(),SumW2Error(kTRUE));
res->Print();
//minNll = res->minNll();
pdf->plotOn(fitFrame,Name("mycurve"));
fitParameters.push_back(3); // nb of fit parameters
fitParameters.push_back(mean.getVal());
fitParameters.push_back(mean.getError());
fitParameters.push_back(sigma.getVal());
fitParameters.push_back(sigma.getError());
fitParameters.push_back(width.getVal());
fitParameters.push_back(width.getError());
}
void PDF_GLWADS_Dpi_K3pi::setUncertainties(config c)
{
switch(c)
{
case lumi1fb:
{
obsErrSource = "1fb-1, ExpNll/sept2012K3PIResult.root";
TString File = this->dir+"/ExpNll/sept2012K3PIResult.root";
TFile *fr = TFile::Open(File);
RooFitResult *r = (RooFitResult*)fr->Get("fitresult_model_reducedData_binned");
assert(r);
for ( int i=0; i<nObs; i++ )
{
RooRealVar* pObs = (RooRealVar*)((RooArgList*)observables)->at(i);
RooRealVar* pRes = (RooRealVar*)r->floatParsFinal().find(obsTmkToMalcolm(pObs->GetName()));
assert(pRes);
StatErr[i] = pRes->getError();
}
SystErr[0] = 0.010; // afav_dpi_obs
SystErr[1] = 0.00011; // rp_dpi_obs
SystErr[2] = 0.00011; // rm_dpi_obs
fr->Close();
delete r;
delete fr;
break;
}
default:
cout << "PDF_GLWADS_Dpi_K3pi::setUncertainties() : ERROR : config "+ConfigToTString(c)+" not found." << endl;
exit(1);
}
}
RooFitResult *breakDownFit(RooSimultaneous *m, RooAbsData *d, RooRealVar *mass, bool precondition = false){
if(precondition){
const char *catsName = m->indexCat().GetName();
TIterator *it = m->indexCat().typeIterator();
while(RooCatType* ci = dynamic_cast<RooCatType*>(it->Next())) {
const Text_t *catLabel = ci->GetName();
RooAbsPdf *pdf = m->getPdf(Form("%s",catLabel));
RooAbsData *reduced = d->reduce(SelectVars(*mass),Cut(Form("%s==%s::%s",catsName, catsName, catLabel)));
RooFitResult *r = pdf->fitTo(*reduced,PrintLevel(-1),Save(),
Minimizer("Minuit2","migrad"),Strategy(0),Hesse(false),Minos(false),Optimize(false)
);
cout << catsName << " " << catLabel << " M2migrad0 " << r->status() << endl;
if(r->status()!=0){
RooFitResult *r = pdf->fitTo(*reduced, PrintLevel(-1), Save());
cout << catsName << " " << catLabel << " Mmigrad1 " << r->status() << endl;
}
}
}
RooFitResult *r = m->fitTo(*d, Save(), PrintLevel(-1),
Strategy(0));
cout << "Global fit Mmigrad0 " << r->status() << endl;
if(r->status()!=0){
RooFitResult *r = m->fitTo(*d, PrintLevel(-1), Save(),
Minimizer("Minuit","minimize"),Strategy(2));
cout << "Global fit Mminimize2 " << r->status() << endl;
return r;
}
return r;
}
void PDF_GLWADS_DKDpi_K3pi::setObservables(config c)
{
switch(c)
{
case truth:{
setObservablesTruth();
break;
}
case toy:{
setObservablesToy();
break;
}
case lumi1fb:{
obsValSource = "1fb-1, ExpNll/sept2012K3PIResult.root";
TString File = this->dir+"/ExpNll/sept2012K3PIResult.root";
TFile *fr = TFile::Open(File);
RooFitResult *r = (RooFitResult*)fr->Get("fitresult_model_reducedData_binned");
assert(r);
TIterator* it = observables->createIterator();
while ( RooRealVar* pObs = (RooRealVar*)it->Next() )
{
RooRealVar* pRes = (RooRealVar*)r->floatParsFinal().find(obsTmkToMalcolm(pObs->GetName()));
pObs->setVal(pRes->getVal());
}
fr->Close();
delete r;
delete fr;
break;
}
case lumi3fb:{
obsValSource = "3fb-1 ANA v7 unblind"; // https://twiki.cern.ch/twiki/pub/LHCbPhysics/B2D0K/LHCb-ANA-2014-071-v7.pdf (see Vavas email 04/08/15)
// these get transformed over from the new inputs using ExpNll/transportGLWADS_new_to_old.py
// in the case of the DK only (robust) combination some of the observables don't exist
// usemap as the temp store
std::map< TString, double > vals;
vals["rkp_k3pi_obs"] = 0.0793;
vals["afav_dk_k3pi_obs"] = -0.0004;
vals["afav_dpi_k3pi_obs"] = 0.0;
vals["rp_dk_k3pi_obs"] = 0.018369;
vals["rm_dk_k3pi_obs"] = 0.009611;
vals["rp_dpi_k3pi_obs"] = 0.003683;
vals["rm_dpi_k3pi_obs"] = 0.003857;
// now can loop the observables and set the values
TIterator* it = observables->createIterator();
while ( RooRealVar* pObs = (RooRealVar*)it->Next() ){
pObs->setVal(vals[pObs->GetName()]);
}
vals.clear();
break;
}
default:{
cout << "PDF_GLWADS_DKDpi_K3pi::setObservables() : ERROR : config "+ConfigToTString(c)+" not found." << endl;
exit(1);
}
}
}
void checkBestFitPoint(std::string workspace, std::string fitFile, bool splusb){
// Open the ws file...
TFile *fd_=0;
TFile *fw_=0;
gSystem->Load("$CMSSW_BASE/lib/$SCRAM_ARCH/libHiggsAnalysisCombinedLimit.so");
gROOT->SetBatch(true);
gStyle->SetOptFit(0);
gStyle->SetOptStat(0);
gStyle->SetPalette(1,0);
fw_ = TFile::Open(workspace.c_str());
w = (RooWorkspace*) fw_->Get("w");
w->Print();
RooDataSet *data = (RooDataSet*) w->data("data_obs");
if (splusb) {
mc_s = (RooStats::ModelConfig*)w->genobj("ModelConfig");
} else {
mc_s = (RooStats::ModelConfig*)w->genobj("ModelConfig_bonly");
}
std::cout << "make nll"<<std::endl;
nll = mc_s->GetPdf()->createNLL(
*data,RooFit::Constrain(*mc_s->GetNuisanceParameters())
,RooFit::Extended(mc_s->GetPdf()->canBeExtended()));
// Now get the best fit result
fd_ = TFile::Open(fitFile.c_str());
RooFitResult *fit;
if (splusb) {
fit =(RooFitResult*)fd_->Get("fit_s");
} else {
fit =(RooFitResult*)fd_->Get("fit_b");
}
RooArgSet fitargs = fit->floatParsFinal();
std::cout << "Got the best fit values" <<std::endl;
w->saveSnapshot("bestfitall",fitargs,true);
TString filename;
if (splusb) {
filename = "minimum_s.pdf";
} else {
filename = "minimum_b.pdf";
}
// Now make the plots!
TCanvas *c = new TCanvas("c","",600,600);
c->SaveAs((filename+"["));
TIterator* iter(fitargs->createIterator());
for (TObject *a = iter->Next(); a != 0; a = iter->Next()) {
RooRealVar *rrv = dynamic_cast<RooRealVar *>(a);
std::string name = rrv->GetName();
TGraph *gr = graphLH(name,rrv->getError());
gr->Draw("ALP");
c->SaveAs((filename+"["));
}
c->SaveAs((filename+"]"));
}
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 rf314_paramfitrange()
{
// D e f i n e o b s e r v a b l e s a n d d e c a y p d f
// ---------------------------------------------------------------
// Declare observables
RooRealVar t("t","t",0,5) ;
RooRealVar tmin("tmin","tmin",0,0,5) ;
// Make parameterized range in t : [tmin,5]
t.setRange(tmin,RooConst(t.getMax())) ;
// Make pdf
RooRealVar tau("tau","tau",-1.54,-10,-0.1) ;
RooExponential model("model","model",t,tau) ;
// C r e a t e i n p u t d a t a
// ------------------------------------
// Generate complete dataset without acceptance cuts (for reference)
RooDataSet* dall = model.generate(t,10000) ;
// Generate a (fake) prototype dataset for acceptance limit values
RooDataSet* tmp = RooGaussian("gmin","gmin",tmin,RooConst(0),RooConst(0.5)).generate(tmin,5000) ;
// Generate dataset with t values that observe (t>tmin)
RooDataSet* dacc = model.generate(t,ProtoData(*tmp)) ;
// F i t p d f t o d a t a i n a c c e p t a n c e r e g i o n
// -----------------------------------------------------------------------
RooFitResult* r = model.fitTo(*dacc,Save()) ;
// P l o t f i t t e d p d f o n f u l l a n d a c c e p t e d d a t a
// ---------------------------------------------------------------------------------
// Make plot frame, add datasets and overlay model
RooPlot* frame = t.frame(Title("Fit to data with per-event acceptance")) ;
dall->plotOn(frame,MarkerColor(kRed),LineColor(kRed)) ;
model.plotOn(frame) ;
dacc->plotOn(frame) ;
// Print fit results to demonstrate absence of bias
r->Print("v") ;
new TCanvas("rf314_paramranges","rf314_paramranges",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
return ;
}
MCMCInterval * Tprime::GetMcmcInterval(ModelConfig mc,
double conf_level,
int n_iter,
int n_burn,
double left_side_tail_fraction,
int n_bins) {
//
// Bayesian MCMC calculation using arbitrary ModelConfig
// Want an efficient proposal function, so derive it from covariance
// matrix of fit
//
RooAbsData * _data = data;
//RooAbsData * _data = pWs->data("obsData");
//RooStats::ModelConfig * _mc = (RooStats::ModelConfig *)pWs->genobj("ModelConfig");
RooStats::ModelConfig * _mc = GetModelConfig();
_mc->Print();
//RooFitResult * fit = pWs->pdf("model_tprime")->fitTo(*_data,Save());
RooFitResult * fit = _mc->GetPdf()->fitTo(*_data,Save());
ProposalHelper ph;
ph.SetVariables((RooArgSet&)fit->floatParsFinal());
ph.SetCovMatrix(fit->covarianceMatrix());
ph.SetUpdateProposalParameters(kTRUE); // auto-create mean vars and add mappings
ph.SetCacheSize(100);
ProposalFunction * pf = ph.GetProposalFunction();
//delete pf;
//pf = new SequentialProposal();
MCMCCalculator mcmc( *_data, mc );
mcmc.SetConfidenceLevel(conf_level);
mcmc.SetNumIters(n_iter); // Metropolis-Hastings algorithm iterations
mcmc.SetProposalFunction(*pf);
mcmc.SetNumBurnInSteps(n_burn); // first N steps to be ignored as burn-in
mcmc.SetLeftSideTailFraction(left_side_tail_fraction);
mcmc.SetNumBins(n_bins);
//mcInt = mcmc.GetInterval();
try {
mcInt = mcmc.GetInterval();
} catch ( std::length_error &ex) {
mcInt = 0;
}
//std::cout << "!!!!!!!!!!!!!! interval" << std::endl;
if (mcInt == 0) std::cout << "No interval found!" << std::endl;
delete fit;
delete pf;
return mcInt;
}
//_________________________________________________
void TestJeffreysGaussSigma(){
// this one is VERY sensitive
// if the Gaussian is narrow ~ range(x)/nbins(x) then the peak isn't resolved
// and you get really bizzare shapes
// if the Gaussian is too wide range(x) ~ sigma then PDF gets renormalized
// and the PDF falls off too fast at high sigma
RooWorkspace w("w");
w.factory("Gaussian::g(x[0,-20,20],mu[0,-5,5],sigma[1,1,5])");
w.factory("n[100,.1,2000]");
w.factory("ExtendPdf::p(g,n)");
// w.var("sigma")->setConstant();
w.var("mu")->setConstant();
w.var("n")->setConstant();
w.var("x")->setBins(301);
RooDataHist* asimov = w.pdf("p")->generateBinned(*w.var("x"),ExpectedData());
RooFitResult* res = w.pdf("p")->fitTo(*asimov,Save(),SumW2Error(kTRUE));
asimov->Print();
res->Print();
TMatrixDSym cov = res->covarianceMatrix();
cout << "variance = " << (cov.Determinant()) << endl;
cout << "stdev = " << sqrt(cov.Determinant()) << endl;
cov.Invert();
cout << "jeffreys = " << sqrt(cov.Determinant()) << endl;
// w.defineSet("poi","mu,sigma");
//w.defineSet("poi","mu,sigma,n");
w.defineSet("poi","sigma");
w.defineSet("obs","x");
RooJeffreysPrior pi("jeffreys","jeffreys",*w.pdf("p"),*w.set("poi"),*w.set("obs"));
// pi.specialIntegratorConfig(kTRUE)->method1D().setLabel("RooAdaptiveGaussKronrodIntegrator1D") ;
pi.specialIntegratorConfig(kTRUE)->getConfigSection("RooIntegrator1D").setRealValue("maxSteps",3);
const RooArgSet* temp = w.set("poi");
pi.getParameters(*temp)->Print();
// return;
// return;
RooGenericPdf* test = new RooGenericPdf("test","test","sqrt(2.)/sigma",*w.set("poi"));
TCanvas* c1 = new TCanvas;
RooPlot* plot = w.var("sigma")->frame();
pi.plotOn(plot);
test->plotOn(plot,LineColor(kRed),LineStyle(kDotted));
plot->Draw();
}
void printResult(){
if (fitresult!=NULL){
printf("%s\n",
"==================================================+++" );
fitresult->Print("v") ;
fitresult->floatParsFinal().Print("s") ;
printf("%s\nfit range: %5.1f %5.1f\n",
"==================================================+++",
min, max );
//RooRealVar* par1_fitresult = (RooRealVar*) fitresult->floatParsFinal()->find("par1")
//par1_fitresult->GetAsymErrorHi() ; // etc...
}
}//printResult
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");
}
///
/// Fills vector with floating pars names
///
void Utils::getParameters(const RooFitResult &result, std::vector<TString> &names){
RooArgList pars = result.floatParsFinal();
TIterator * it = pars.createIterator();
while(RooRealVar* p = (RooRealVar*) it->Next()){
names.push_back(TString(p->GetName()));
}
};
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;
}
RooFitResult* FitHistWithCBShape(TH1* h, double mass)
{
RooFitResult *res = 0;
double avg = 0.97*mass;
double rms = 0.05*mass;
double xmin = 0.7*mass;
// double xmax = 1.25*mass;
// double xmin = 0.80*mass;
double xmax = 1.25*mass;
RooRealVar E("E","Normalised Resonance Mass", xmin, xmax, "GeV");
RooDataHist dataSet("dataSet", "dataSet", E, h );
RooPlot* frame = E.frame();
dataSet.plotOn(frame);
RooRealVar mean("mean","mean", avg, 0.92*mass, 1.2*mass) ;
RooRealVar sigma("sigma","width", rms, 0.001*mass, 0.2*mass);
RooRealVar alphaLow("alphaLow","alpha", 0.8, 0.0, 3.0);
RooRealVar nLow("nLow","n", 20, 0.00001, 1000.0);
RooRealVar alphaHigh("alphaHigh","alphaHigh", 2.0, 0.0, 3.0);
RooRealVar nHigh("nHigh","nHigh", 100.0, 0.0000000001, 1000.0);
RooFormulaVar minusE("minusE","Negate the data set","-1*E", E) ;
RooFormulaVar minusMean("minusMean","Negate the mean","-1*mean", mean) ;
RooCBShape CBallLow("CBallLow","Crystal Ball for lower tail", E, mean, sigma, alphaLow, nLow);
RooCBShape CBallHigh("CBallHigh","Crystal Ball for highr tail", minusE, minusMean, sigma, alphaHigh, nHigh);
RooRealVar cb1frac("cb1frac","fraction of cb1", 0.5, 0, 1.0) ;
RooAddPdf sum("sum","cb1+cb2",RooArgList(CBallLow, CBallHigh), cb1frac) ;
res = sum.fitTo(dataSet, Save() );
sum.plotOn(frame);
frame->Draw();
double sMean = ((RooAbsReal*) res->floatParsFinal().find("mean"))->getVal();
double sSigma = ((RooAbsReal*) res->floatParsFinal().find("sigma"))->getVal();
double sAlphaLow = ((RooAbsReal*) res->floatParsFinal().find("alphaLow"))->getVal();
double sAlphaHigh = ((RooAbsReal*) res->floatParsFinal().find("alphaHigh"))->getVal();
double sNLow = ((RooAbsReal*) res->floatParsFinal().find("nLow"))->getVal();
double sNHigh = ((RooAbsReal*) res->floatParsFinal().find("nHigh"))->getVal();
double sFrac = ((RooAbsReal*) res->floatParsFinal().find("cb1frac"))->getVal();
Masses.push_back(mass); Means->push_back(sMean); Sigmas->push_back(sSigma);
alphaHights->push_back(sAlphaHigh), alphaLows->push_back(sAlphaLow);
nHighs->push_back(sNHigh), nLows->push_back(sNLow), fracs->push_back(sFrac);
return res;
}
void JeffreysPriorDemo(){
RooWorkspace w("w");
w.factory("Uniform::u(x[0,1])");
w.factory("mu[100,1,200]");
w.factory("ExtendPdf::p(u,mu)");
// w.factory("Poisson::pois(n[0,inf],mu)");
RooDataHist* asimov = w.pdf("p")->generateBinned(*w.var("x"),ExpectedData());
// RooDataHist* asimov2 = w.pdf("pois")->generateBinned(*w.var("n"),ExpectedData());
RooFitResult* res = w.pdf("p")->fitTo(*asimov,Save(),SumW2Error(kTRUE));
asimov->Print();
res->Print();
TMatrixDSym cov = res->covarianceMatrix();
cout << "variance = " << (cov.Determinant()) << endl;
cout << "stdev = " << sqrt(cov.Determinant()) << endl;
cov.Invert();
cout << "jeffreys = " << sqrt(cov.Determinant()) << endl;
w.defineSet("poi","mu");
w.defineSet("obs","x");
// w.defineSet("obs2","n");
RooJeffreysPrior pi("jeffreys","jeffreys",*w.pdf("p"),*w.set("poi"),*w.set("obs"));
// pi.specialIntegratorConfig(kTRUE)->method1D().setLabel("RooAdaptiveGaussKronrodIntegrator1D") ;
// pi.specialIntegratorConfig(kTRUE)->getConfigSection("RooIntegrator1D").setRealValue("maxSteps",10);
// JeffreysPrior pi2("jeffreys2","jeffreys",*w.pdf("pois"),*w.set("poi"),*w.set("obs2"));
// return;
RooGenericPdf* test = new RooGenericPdf("test","test","1./sqrt(mu)",*w.set("poi"));
TCanvas* c1 = new TCanvas;
RooPlot* plot = w.var("mu")->frame();
// pi.plotOn(plot, Normalization(1,RooAbsReal::Raw),Precision(.1));
pi.plotOn(plot);
// pi2.plotOn(plot,LineColor(kGreen),LineStyle(kDotted));
test->plotOn(plot,LineColor(kRed));
plot->Draw();
}
//_________________________________________________
void TestJeffreysGaussMean(){
RooWorkspace w("w");
w.factory("Gaussian::g(x[0,-20,20],mu[0,-5,5],sigma[1,0,10])");
w.factory("n[10,.1,200]");
w.factory("ExtendPdf::p(g,n)");
w.var("sigma")->setConstant();
w.var("n")->setConstant();
RooDataHist* asimov = w.pdf("p")->generateBinned(*w.var("x"),ExpectedData());
RooFitResult* res = w.pdf("p")->fitTo(*asimov,Save(),SumW2Error(kTRUE));
asimov->Print();
res->Print();
TMatrixDSym cov = res->covarianceMatrix();
cout << "variance = " << (cov.Determinant()) << endl;
cout << "stdev = " << sqrt(cov.Determinant()) << endl;
cov.Invert();
cout << "jeffreys = " << sqrt(cov.Determinant()) << endl;
// w.defineSet("poi","mu,sigma");
w.defineSet("poi","mu");
w.defineSet("obs","x");
RooJeffreysPrior pi("jeffreys","jeffreys",*w.pdf("p"),*w.set("poi"),*w.set("obs"));
// pi.specialIntegratorConfig(kTRUE)->method1D().setLabel("RooAdaptiveGaussKronrodIntegrator1D") ;
// pi.specialIntegratorConfig(kTRUE)->getConfigSection("RooIntegrator1D").setRealValue("maxSteps",3);
const RooArgSet* temp = w.set("poi");
pi.getParameters(*temp)->Print();
// return;
RooGenericPdf* test = new RooGenericPdf("test","test","1",*w.set("poi"));
TCanvas* c1 = new TCanvas;
RooPlot* plot = w.var("mu")->frame();
pi.plotOn(plot);
test->plotOn(plot,LineColor(kRed),LineStyle(kDotted));
plot->Draw();
}
MCMCInterval * TwoBody::GetMcmcInterval_OldWay(ModelConfig mc,
double conf_level,
int n_iter,
int n_burn,
double left_side_tail_fraction,
int n_bins){
// use MCMCCalculator (takes about 1 min)
// Want an efficient proposal function, so derive it from covariance
// matrix of fit
RooFitResult* fit = ws->pdf("model")->fitTo(*data,Save());
ProposalHelper ph;
ph.SetVariables((RooArgSet&)fit->floatParsFinal());
ph.SetCovMatrix(fit->covarianceMatrix());
ph.SetUpdateProposalParameters(kTRUE); // auto-create mean vars and add mappings
ph.SetCacheSize(100);
ProposalFunction* pf = ph.GetProposalFunction();
MCMCCalculator mcmc( *data, mc );
mcmc.SetConfidenceLevel(conf_level);
mcmc.SetNumIters(n_iter); // Metropolis-Hastings algorithm iterations
mcmc.SetProposalFunction(*pf);
mcmc.SetNumBurnInSteps(n_burn); // first N steps to be ignored as burn-in
mcmc.SetLeftSideTailFraction(left_side_tail_fraction);
mcmc.SetNumBins(n_bins);
//mcInt = mcmc.GetInterval();
try {
mcInt = mcmc.GetInterval();
} catch ( std::length_error &ex) {
mcInt = 0;
}
//std::cout << "!!!!!!!!!!!!!! interval" << std::endl;
if (mcInt == 0) std::cout << "No interval found!" << std::endl;
return mcInt;
}
RooFitResult* fitter_Zee(TH1D *hist){
RooRealVar Zmassvar("Zmassvar","Zmassvar", 82, 100);
RooDataHist *datahist = new RooDataHist("data","Z Mass",Zmassvar,hist);
RooPlot *Zmassvarframe = Zmassvar.frame(Name("Zmassvarframe"),Title(hist->GetTitle())) ;
datahist->plotOn(Zmassvarframe);
RooRealVar alpha ("alpha" , "alpha" , 0.005,0.001,0.1);
RooRealVar n ("n" , "n" , 1,0.001,10);
RooRealVar cbmean ("cbmean" , "cbmean" , 1, 0.8, 1.2);
RooRealVar cbsigma("cbsigma", "cbsigma" , 0.01, 0.001, 0.2);
RooRealVar bwmean("bwmean","bwmean",91,85,95);
RooRealVar bwsigma("bwsigma","bwsigma",3,2,4);
RooRealVar expoconst("expoconst","expoconst",-0.1,-0.5,0);
RooCBShape cball ("cball" , "crystal ball" , Zmassvar, cbmean, cbsigma, alpha, n);
RooBreitWigner bw("bw","breit wigner",Zmassvar,bwmean,bwsigma);
RooFFTConvPdf cballXbw("cballXbw","cball (X) bw",Zmassvar,bw,cball);
RooExponential expo("expo", "exponential", Zmassvar, expoconst);
RooRealVar frac("frac","frac",0.1,0.001,0.2);
RooAddPdf Zshapemodel("Zshapemodel","expo + cball (X) bw",RooArgList(expo,cballXbw),frac);
RooFitResult *fitres =Zshapemodel.fitTo(*datahist,Range(82,100),Save());
Zshapemodel.plotOn(Zmassvarframe,LineColor(kBlue));
Zmassvarframe->Draw();
fitres->Print();
return fitres;
};
void SHyFT::fit(bool verbose)
{
if ( verbose ) cout << "Fitting" << endl;
RooArgSet nllset;
for(unsigned int i=0;i<bins_.size();++i) {
nllset.add(*bins_[i]->nll());
}
RooAddition nllsum("nllsum","nllsum",nllset);
RooMinuit m(nllsum);
if ( verbose ) m.setVerbose(kTRUE);
else {
m.setVerbose(kFALSE);
m.setPrintLevel(-1);
}
m.migrad();
m.hesse();
// m.minos();
if ( verbose ) {
RooFitResult * f = m.save();
f->Print("v");
}
}
///
/// Fit a pdf to the minimum, but keep angular parameters in a range of
/// [0,2pi]. If after an initial fit, a parameter has walked outside this
/// interval, add multiples of 2pi to bring it back. Then, refit.
/// All variables that have unit 'rad' are taken to be angles.
///
RooFitResult* Utils::fitToMinBringBackAngles(RooAbsPdf *pdf, bool thorough, int printLevel)
{
countAllFitBringBackAngle++;
RooFitResult* r = fitToMin(pdf, thorough, printLevel);
bool refit = false;
TIterator* it = r->floatParsFinal().createIterator();
while ( RooRealVar* p = (RooRealVar*)it->Next() ){
if ( ! isAngle(p) ) continue;
if ( p->getVal()<0.0 || p->getVal()>2.*TMath::Pi() ){
RooArgSet *pdfPars = pdf->getParameters(RooArgSet());
RooRealVar *pdfPar = (RooRealVar*)pdfPars->find(p->GetName());
pdfPar->setVal(bringBackAngle(p->getVal()));
refit = true;
delete pdfPars;
}
}
if ( refit ){
countFitBringBackAngle++;
delete r;
r = fitToMin(pdf, thorough, printLevel);
}
delete it;
return r;
}
///
/// Test PDF implementation.
/// Performs a fit to the minimum.
///
bool PDF_Abs::test()
{
bool quiet = false;
if(quiet) RooMsgService::instance().setGlobalKillBelow(ERROR);
fixParameters(observables);
floatParameters(parameters);
setLimit(parameters, "free");
RooFormulaVar ll("ll", "ll", "-2*log(@0)", RooArgSet(*pdf));
RooMinuit m(ll);
if(quiet) m.setPrintLevel(-2);
m.setNoWarn();
m.setLogFile("/dev/zero");
m.setErrorLevel(1.0);
m.setStrategy(2);
// m.setProfile(1);
m.migrad();
RooFitResult *f = m.save();
bool status = !(f->edm()<1 && f->status()==0);
if(!quiet) f->Print("v");
delete f;
if(quiet) RooMsgService::instance().setGlobalKillBelow(INFO);
if(!quiet) cout << "pdf->getVal() = " << pdf->getVal() << endl;
return status;
}
TString* saveResult(const char *filename){
// printf(" current dir == %s\n" , gDirectory->GetPath());
TDirectory *curr=(TDirectory*)gDirectory;
//======= TIME AND DATE ===============
time_t curtime; struct tm *loctime;
char ch[200];
char chroof[200];
curtime = time (NULL); loctime = localtime (&curtime);
// TString sr=s.Data(); // commandbox contents
// if ( sr.CompareTo("")==0 ){
sprintf(ch,"fit%04d%02d%02d_%02d%02d%02d",
1900+loctime->tm_year, 1+loctime->tm_mon,
loctime->tm_mday, loctime->tm_hour, loctime->tm_min, loctime->tm_sec);
sprintf(chroof,"roo%04d%02d%02d_%02d%02d%02d",
1900+loctime->tm_year, 1+loctime->tm_mon,
loctime->tm_mday, loctime->tm_hour, loctime->tm_min, loctime->tm_sec);
TString *stamp=new TString(ch); //tpad fit HERE
// }else{
// sprintf(ch,"shspe%04d%02d%02d_%02d%02d%02d_%s_.root",
// 1900+loctime->tm_year, 1+loctime->tm_mon,
// loctime->tm_mday, loctime->tm_hour, loctime->tm_min, loctime->tm_sec,
// sr.Data() );
// }
// printf("! ... saving RooFitResult into /%s/\n", filename );
printf("i ... names: /%s/ \n" , chroof );
TFile f(filename,"UPDATE") ;
fitresult->Write( chroof ) ; //write RooFitResult fitresult je GLOBALNI
f.Close() ;
curr->cd();
// printf("i ... returned to current dir == %s\n" , gDirectory->GetPath());
return stamp;
}//saveresult...
void StandardBayesianNumericalDemo(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData") {
// option definitions
double confLevel = optBayes.confLevel;
TString integrationType = optBayes.integrationType;
int nToys = optBayes.nToys;
bool scanPosterior = optBayes.scanPosterior;
int nScanPoints = optBayes.nScanPoints;
int intervalType = optBayes.intervalType;
int maxPOI = optBayes.maxPOI;
double nSigmaNuisance = optBayes.nSigmaNuisance;
/////////////////////////////////////////////////////////////
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
////////////////////////////////////////////////////////////
const char* filename = "";
if (!strcmp(infile,"")) {
filename = "results/example_combined_GaussExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
return;
#endif
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
}
else
filename = infile;
// Try to open the file
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file ){
cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return;
}
/////////////////////////////////////////////////////////////
// Tutorial starts here
////////////////////////////////////////////////////////////
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
/////////////////////////////////////////////
// create and use the BayesianCalculator
// to find and plot the 95% credible interval
// on the parameter of interest as specified
// in the model config
// before we do that, we must specify our prior
// it belongs in the model config, but it may not have
// been specified
RooUniform prior("prior","",*mc->GetParametersOfInterest());
w->import(prior);
mc->SetPriorPdf(*w->pdf("prior"));
// do without systematics
//mc->SetNuisanceParameters(RooArgSet() );
if (nSigmaNuisance > 0) {
RooAbsPdf * pdf = mc->GetPdf();
assert(pdf);
RooFitResult * res = pdf->fitTo(*data, Save(true), Minimizer(ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str()), Hesse(true),
PrintLevel(ROOT::Math::MinimizerOptions::DefaultPrintLevel()-1) );
res->Print();
RooArgList nuisPar(*mc->GetNuisanceParameters());
for (int i = 0; i < nuisPar.getSize(); ++i) {
RooRealVar * v = dynamic_cast<RooRealVar*> (&nuisPar[i] );
assert( v);
v->setMin( TMath::Max( v->getMin(), v->getVal() - nSigmaNuisance * v->getError() ) );
v->setMax( TMath::Min( v->getMax(), v->getVal() + nSigmaNuisance * v->getError() ) );
std::cout << "setting interval for nuisance " << v->GetName() << " : [ " << v->getMin() << " , " << v->getMax() << " ]" << std::endl;
}
}
BayesianCalculator bayesianCalc(*data,*mc);
bayesianCalc.SetConfidenceLevel(confLevel); // 95% interval
// default of the calculator is central interval. here use shortest , central or upper limit depending on input
// doing a shortest interval might require a longer time since it requires a scan of the posterior function
if (intervalType == 0) bayesianCalc.SetShortestInterval(); // for shortest interval
if (intervalType == 1) bayesianCalc.SetLeftSideTailFraction(0.5); // for central interval
if (intervalType == 2) bayesianCalc.SetLeftSideTailFraction(0.); // for upper limit
if (!integrationType.IsNull() ) {
bayesianCalc.SetIntegrationType(integrationType); // set integrationType
bayesianCalc.SetNumIters(nToys); // set number of ietrations (i.e. number of toys for MC integrations)
}
// in case of toyMC make a nnuisance pdf
if (integrationType.Contains("TOYMC") ) {
RooAbsPdf * nuisPdf = RooStats::MakeNuisancePdf(*mc, "nuisance_pdf");
cout << "using TOYMC integration: make nuisance pdf from the model " << std::endl;
nuisPdf->Print();
bayesianCalc.ForceNuisancePdf(*nuisPdf);
scanPosterior = true; // for ToyMC the posterior is scanned anyway so used given points
}
// compute interval by scanning the posterior function
if (scanPosterior)
bayesianCalc.SetScanOfPosterior(nScanPoints);
RooRealVar* poi = (RooRealVar*) mc->GetParametersOfInterest()->first();
if (maxPOI != -999 && maxPOI > poi->getMin())
poi->setMax(maxPOI);
SimpleInterval* interval = bayesianCalc.GetInterval();
// print out the iterval on the first Parameter of Interest
cout << "\n>>>> RESULT : " << confLevel*100 << "% interval on " << poi->GetName()<<" is : ["<<
interval->LowerLimit() << ", "<<
interval->UpperLimit() <<"] "<<endl;
// make a plot
// since plotting may take a long time (it requires evaluating
// the posterior in many points) this command will speed up
// by reducing the number of points to plot - do 50
// ignore errors of PDF if is zero
RooAbsReal::setEvalErrorLoggingMode(RooAbsReal::Ignore) ;
cout << "\nDrawing plot of posterior function....." << endl;
// always plot using numer of scan points
bayesianCalc.SetScanOfPosterior(nScanPoints);
RooPlot * plot = bayesianCalc.GetPosteriorPlot();
plot->Draw();
}
void fitSignalShapeW(int massBin,int id, int channels,int categ, int sample,
/* float lumi, bool doSfLepton, */double rangeLow, double rangeHigh,
double bwSigma,
double fitValues[9], double fitErrors[9], double covQual[1]){
// ------ root settings ---------
gROOT->Reset();
gROOT->SetStyle("Plain");
gStyle->SetPadGridX(kFALSE);
gStyle->SetPadGridY(kFALSE);
//gStyle->SetOptStat("kKsSiourRmMen");
gStyle->SetOptStat("iourme");
//gStyle->SetOptStat("rme");
//gStyle->SetOptStat("");
gStyle->SetOptFit(11);
gStyle->SetPadLeftMargin(0.14);
gStyle->SetPadRightMargin(0.06);
// ------------------------------
ROOT::Math::MinimizerOptions::SetDefaultTolerance( 1.E-7);
stringstream FileName;
//Insert the file here
if(sample==1) FileName <<"root://lxcms03//data3/Higgs/150915/ZH125/ZZ4lAnalysis.root" ;
else if(sample==2) FileName << "root://lxcms03//data3/Higgs/150915/WplusH125/ZZ4lAnalysis.root";
else if(sample==3) FileName << "root://lxcms03//data3/Higgs/150915/WminusH125/ZZ4lAnalysis.root";
else if(sample==4) FileName << "root://lxcms03//data3/Higgs/150915/ttH125/ZZ4lAnalysis.root";
else {
cout << "Wrong sample." << endl;
return;
}
cout << "Using " << FileName.str() << endl;
TFile* ggFile = TFile::Open(FileName.str().c_str());
TTree* ggTree = (TTree*) ggFile->Get("ZZTree/candTree");
float m4l;
Short_t z1flav, z2flav;
float weight;
Short_t nExtraLeptons;
float ZZPt;
Short_t nJets;
Short_t nBTaggedJets;
std::vector<float> * jetpt = 0;
std::vector<float> * jeteta = 0;
std::vector<float> * jetphi = 0;
std::vector<float> * jetmass = 0;
float jet30pt[10];
float jet30eta[10];
float jet30phi[10];
float jet30mass[10];
float Fisher;
int nentries = ggTree->GetEntries();
//--- ggTree part
ggTree->SetBranchAddress("ZZMass",&m4l);
ggTree->SetBranchAddress("Z1Flav",&z1flav);
ggTree->SetBranchAddress("Z2Flav",&z2flav);
ggTree->SetBranchAddress("genHEPMCweight",&weight);
ggTree->SetBranchAddress("nExtraLep",&nExtraLeptons);
ggTree->SetBranchAddress("nCleanedJets",&nJets);
ggTree->SetBranchAddress("nCleanedJetsPt30BTagged",&nBTaggedJets);
ggTree->SetBranchAddress("DiJetFisher",&Fisher);
ggTree->SetBranchAddress("JetPt",&jetpt);
ggTree->SetBranchAddress("JetEta",&jeteta);
ggTree->SetBranchAddress("JetPhi",&jetphi);
ggTree->SetBranchAddress("JetMass",&jetmass);
ggTree->SetBranchAddress("ZZPt",&ZZPt);
//--- rooFit part
double xMin,xMax,xInit;
xInit = (double) massBin;
xMin = rangeLow;
xMax = rangeHigh ;
cout << "Fit range: [" << xMin << " , " << xMax << "]. Init value = " << xInit << endl;
TH1F *hmass = new TH1F("hmass","hmass",200,xMin,xMax);
//---------
RooRealVar x("mass","m_{4l}",xInit,xMin,xMax,"GeV");
RooRealVar w("myW","myW",1.0,0.,1000.);
RooArgSet ntupleVarSet(x,w);
RooDataSet dataset("mass4l","mass4l",ntupleVarSet,WeightVar("myW"));
for(int k=0; k<nentries; k++){
ggTree->GetEvent(k);
int njet30 = 0;
for (unsigned int ijet = 0; ijet < jetpt->size(); ijet++) {
if ( (*jetpt)[ijet] > 30. ) {
jet30pt[njet30] = (*jetpt)[ijet];
jet30eta[njet30] = (*jeteta)[ijet];
jet30phi[njet30] = (*jetphi)[ijet];
jet30mass[njet30] = (*jetmass)[ijet];
njet30++;
}
}
int Cat = category(nExtraLeptons, ZZPt, m4l, njet30, nBTaggedJets, jet30pt, jet30eta, jet30phi,jet30mass, Fisher);
if (categ >= 0 && categ != Cat ) continue;
if(channels==0 && z1flav*z2flav != 28561) continue;
if(channels==1 && z1flav*z2flav != 14641) continue;
if (weight <= 0 ) cout << "Warning! Negative weight events" << endl;
if(channels==2 && z1flav*z2flav != 20449) continue;
ntupleVarSet.setRealValue("mass",m4l);
ntupleVarSet.setRealValue("myW",weight);
if(x.getVal()>xMin && x.getVal()<xMax)
dataset.add(ntupleVarSet, weight);
hmass->Fill(m4l);
}
//---------
cout << "dataset n entries: " << dataset.sumEntries() << endl;
TCanvas *c1 = new TCanvas("c1","c1",725,725);
c1->cd();
TPad *pad1 = new TPad("pad1","This is pad1",0.05,0.35,0.95,0.97);
pad1->Draw();
TPad *pad2 = new TPad("pad2","This is pad2",0.05,0.02,0.95,0.35);
pad2->Draw();
//--- double CrystalBall
RooRealVar mean("bias","mean of gaussian",0,-5.,5.) ;
RooRealVar sigma("sigma","width of gaussian",1.5,0.,30.);
RooRealVar a1("a1","a1",1.46,0.5,5.);
RooRealVar n1("n1","n1",1.92,0.,10.);
RooRealVar a2("a2","a2",1.46,1.,10.);
RooRealVar n2("n2","n2",20,1.,50.);
RooDoubleCB DCBall("DCBall","Double Crystal ball",x,mean,sigma,a1,n1,a2,n2);
if (channels== 1) mean.setVal(-1.);
//--- Breit-Wigner
float bwSigmaMax,bwSigmaMin;
if(massBin<400) bwSigmaMin=bwSigmaMax=bwSigma;
else {
bwSigmaMin=bwSigma-20.;
bwSigmaMax=bwSigma+20.;
}
RooRealVar mean3("mean3","mean3",xInit) ;
RooRealVar sigma3("sigma3","width3",bwSigma,bwSigmaMin,bwSigmaMax);
RooRealVar scale3("scale3","scale3 ",1.);
RooRelBWUFParam bw("bw","bw",x,mean3,scale3);
//Chebyshev-Polynomial
RooRealVar A1("A1","A1",-1,-3,3.);
RooRealVar A2("A2","A2",0.5,-3.,3.);
RooChebychev BkgPDF("BkgPDF","BkgPDF",x ,RooArgList(A1,A2));
//Fraction
RooRealVar frac("frac","Fraction for PDF",0.5,0.,1.);
x.setBins(10000,"fft");
RooFFTConvPdf model("model","model",x,bw,DCBall);
RooAddPdf totPDF("totPDF","Total PDF ",RooArgList(model,BkgPDF),RooArgList(frac));
RooArgSet* params = totPDF.getParameters(x);
if(sample!=1 && categ!=0 && id!=125){
if(channels==0 ){params->readFromFile("Ch0_Cat0_paraT.txt");}// Read the Parameter for the Resonance + Bkg(ChebyChev)
if(channels==1 ){params->readFromFile("Ch1_Cat0_paraT.txt");}// Read the Parameter for the Resonance + Bkg(ChebyChev)
if(channels==2 ){params->readFromFile("Ch2_Cat0_paraT.txt");}// Read the Parameter for the Resonance + Bkg(ChebyChev)
}
RooFitResult *fitres = (RooFitResult*)totPDF.fitTo(dataset,SumW2Error(1),Range(xMin,xMax),Strategy(2),NumCPU(8),Save(true));
if (sample==1 && categ==0 && id==125){
mean.setConstant(kTRUE);
sigma.setConstant(kTRUE);
a1.setConstant(kTRUE);
n1.setConstant(kTRUE);
a2.setConstant(kTRUE);
n2.setConstant(kTRUE);
mean3.setConstant(kTRUE);
sigma3.setConstant(kTRUE);
scale3.setConstant(kTRUE);
A1.setConstant(kTRUE);
A2.setConstant(kTRUE);
frac.setConstant(kTRUE);
if(channels==0 ){
params->readFromFile("Ch0_Cat0_para.txt"); // Read the Parameter for the Resonance as ggH sample
params->writeToFile("Ch0_Cat0_paraT.txt");} // Writing the Parameter for Full PDF including the Chebyshev-Polynomial
if(channels==1 )
{params->readFromFile("Ch1_Cat0_para.txt"); // Read the Parameter for the Resonance as in ggH sample
params->writeToFile("Ch1_Cat0_paraT.txt");}// Writing the Parameter for Full PDF including the Chebyshev-Polynomial
if(channels==2 ){
params->readFromFile("Ch2_Cat0_para.txt"); // Read the Parameter for the Resonance as ggH sample
params->writeToFile("Ch2_Cat0_paraT.txt");}// Writing the Parameter for Full PDF including the Chebyshev-Polynomial
}
stringstream frameTitle;
if(channels==0){frameTitle << "4#mu, m_{H} = "; }
if(channels==1){frameTitle << "4e, m_{H} = ";}
if(channels==2){frameTitle << "2e2#mu, m_{H} = ";}
frameTitle << massBin << " GeV";
stringstream nameFileRoot;
nameFileRoot << "fitM" << massBin << ".root";
TFile *fileplot = TFile::Open(nameFileRoot.str().c_str(), "recreate");
RooPlot* xframe = x.frame() ;
xframe->SetTitle("");
xframe->SetName("m4lplot");
dataset.plotOn(xframe,DataError(RooAbsData::SumW2), MarkerStyle(kOpenCircle), MarkerSize(1.1) );
int col;
if(channels==0) col=kOrange+7;
if(channels==1) col=kAzure+2;
if(channels==2) col=kGreen+3;
totPDF.plotOn(xframe,LineColor(col));
RooHist* hpull = xframe->pullHist();
RooPlot* frame3 = x.frame(Title("Pull Distribution")) ;
frame3->addPlotable(hpull,"P");
// cosmetics
TLegend *legend = new TLegend(0.20,0.45,0.45,0.60,NULL,"brNDC");
legend->SetBorderSize(0);
legend->SetFillColor(0);
legend->SetTextAlign(12);
legend->SetTextFont (42);
legend->SetTextSize (0.03);
TH1F *dummyPoints = new TH1F("dummyP","dummyP",1,0,1);
TH1F *dummyLine = new TH1F("dummyL","dummyL",1,0,1);
dummyPoints->SetMarkerStyle(kOpenCircle);
dummyPoints->SetMarkerSize(1.1);
dummyLine->SetLineColor(col);
legend->AddEntry(dummyPoints, "Simulation", "pe");
legend->AddEntry(dummyLine, "Parametric Model", "l");
TPaveText *text = new TPaveText(0.15,0.90,0.77,0.98,"brNDC");
text->AddText("CMS Simulation");
text->SetBorderSize(0);
text->SetFillStyle(0);
text->SetTextAlign(12);
text->SetTextFont(42);
text->SetTextSize(0.03);
TPaveText *titlet = new TPaveText(0.15,0.80,0.60,0.85,"brNDC");
titlet->AddText(frameTitle.str().c_str());
titlet->SetBorderSize(0);
titlet->SetFillStyle(0);
titlet->SetTextAlign(12);
titlet->SetTextFont(132);
titlet->SetTextSize(0.045);
TPaveText *sigmat = new TPaveText(0.15,0.65,0.77,0.78,"brNDC");
stringstream sigmaval0, sigmaval1, sigmaval2;
sigmaval0 << fixed;
sigmaval0 << setprecision(1);
sigmaval0 << "m_{dCB} = " << mean.getVal() + massBin << " GeV";
sigmaval1 << fixed;
sigmaval1 << setprecision(1);
sigmaval1 << "#sigma_{dCB} = " << sigma.getVal() << " GeV";
sigmaval2 << fixed;
sigmaval2 << setprecision(1);
sigmaval2 << "RMS_{eff} = " << effSigma(hmass) << " GeV";
sigmat->AddText(sigmaval1.str().c_str());
sigmat->AddText(sigmaval2.str().c_str());
sigmat->SetBorderSize(0);
sigmat->SetFillStyle(0);
sigmat->SetTextAlign(12);
sigmat->SetTextFont(132);
sigmat->SetTextSize(0.04);
xframe->GetYaxis()->SetTitleOffset(1.5);
cout << "EFF RMS = " << effSigma(hmass) << " RMS = " << hmass->GetRMS() << endl;
pad1->cd();
stringstream nameFile, nameFileC, nameFilePng;
nameFile << "fitM" << massBin << "_channel" << channels<< "_category"<< categ << ".pdf";
nameFileC << "fitM" << massBin << "_channel" << channels << "_category"<< categ << ".C";
nameFilePng << "fitM" << massBin << "_channel" << channels << "_category"<< categ << ".png";
xframe->Draw();
gPad->Update(); legend->Draw(); text->Draw(); sigmat->Draw(); titlet->Draw();
pad2->cd() ;
frame3->Draw() ;
frame3->SetMinimum(-3);
frame3->SetMaximum(3);
TLine *line1 = new TLine(105,0,140,0);
line1->SetLineColor(kRed);
line1->Draw();
c1->Print(nameFile.str().c_str());
c1->SaveAs(nameFileC.str().c_str());
c1->SaveAs(nameFilePng.str().c_str());
fileplot->cd();
xframe->Write();
sigmat->Write();
hmass->Write();
fileplot->Close();
if(fitValues!=0){
fitValues[0] = a1.getVal();
fitValues[1] = a2.getVal();
fitValues[2] = mean.getVal();
fitValues[3] = mean3.getVal();
fitValues[4] = n1.getVal();
fitValues[5] = n2.getVal();
fitValues[6] = sigma.getVal();
fitValues[7] = A1.getVal();
fitValues[8] = A2.getVal();
}
if(fitErrors!=0){
fitErrors[0] = a1.getError();
fitErrors[1] = a2.getError();
fitErrors[2] = mean.getError();
fitErrors[3] = mean3.getError();
fitErrors[4] = n1.getError();
fitErrors[5] = n2.getError();
fitErrors[6] = sigma.getError();
fitErrors[7] = A1.getError();
fitErrors[8] = A2.getError();
}
covQual[0] = fitres->covQual();
}
int KinZfitter::PerZ1Likelihood(double & l1, double & l2, double & lph1, double & lph2)
{
l1= 1.0; l2 = 1.0;
lph1 = 1.0; lph2 = 1.0;
if(debug_) cout<<"start Z1 refit"<<endl;
TLorentzVector Z1_1 = p4sZ1_[0]; TLorentzVector Z1_2 = p4sZ1_[1];
double RECOpT1 = Z1_1.Pt(); double RECOpT2 = Z1_2.Pt();
double pTerrZ1_1 = pTerrsZ1_[0]; double pTerrZ1_2 = pTerrsZ1_[1];
if(debug_)cout<<"pT1 "<<RECOpT1<<" pTerrZ1_1 "<<pTerrZ1_1<<endl;
if(debug_)cout<<"pT2 "<<RECOpT2<<" pTerrZ1_2 "<<pTerrZ1_2<<endl;
//////////////
TLorentzVector Z1_ph1, Z1_ph2;
double pTerrZ1_ph1, pTerrZ1_ph2;
double RECOpTph1, RECOpTph2;
TLorentzVector nullFourVector(0, 0, 0, 0);
Z1_ph1=nullFourVector; Z1_ph2=nullFourVector;
RECOpTph1 = 0; RECOpTph2 = 0;
pTerrZ1_ph1 = 0; pTerrZ1_ph2 = 0;
if(p4sZ1ph_.size()>=1){
Z1_ph1 = p4sZ1ph_[0]; pTerrZ1_ph1 = pTerrsZ1ph_[0];
RECOpTph1 = Z1_ph1.Pt();
if(debug_) cout<<"put in Z1 fsr photon 1 pT "<<RECOpTph1<<" pT err "<<pTerrZ1_ph1<<endl;
}
if(p4sZ1ph_.size()==2){
//if(debug_) cout<<"put in Z1 fsr photon 2"<<endl;
Z1_ph2 = p4sZ1ph_[1]; pTerrZ1_ph2 = pTerrsZ1ph_[1];
RECOpTph2 = Z1_ph2.Pt();
}
RooRealVar* pT1RECO = new RooRealVar("pT1RECO","pT1RECO", RECOpT1, 5, 500);
RooRealVar* pT2RECO = new RooRealVar("pT2RECO","pT2RECO", RECOpT2, 5, 500);
double RECOpT1min = max(5.0, RECOpT1-2*pTerrZ1_1);
double RECOpT2min = max(5.0, RECOpT2-2*pTerrZ1_2);
RooRealVar* pTph1RECO = new RooRealVar("pTph1RECO","pTph1RECO", RECOpTph1, 5, 500);
RooRealVar* pTph2RECO = new RooRealVar("pTph2RECO","pTph2RECO", RECOpTph2, 5, 500);
double RECOpTph1min = max(0.5, RECOpTph1-2*pTerrZ1_ph1);
double RECOpTph2min = max(0.5, RECOpTph2-2*pTerrZ1_ph2);
// observables pT1,2,ph1,ph2
RooRealVar* pT1 = new RooRealVar("pT1", "pT1FIT", RECOpT1, RECOpT1min, RECOpT1+2*pTerrZ1_1 );
RooRealVar* pT2 = new RooRealVar("pT2", "pT2FIT", RECOpT2, RECOpT2min, RECOpT2+2*pTerrZ1_2 );
RooRealVar* m1 = new RooRealVar("m1","m1", Z1_1.M());
RooRealVar* m2 = new RooRealVar("m2","m2", Z1_2.M());
if(debug_) cout<<"m1 "<<m1->getVal()<<" m2 "<<m2->getVal()<<endl;
double Vtheta1, Vphi1, Vtheta2, Vphi2;
Vtheta1 = (Z1_1).Theta(); Vtheta2 = (Z1_2).Theta();
Vphi1 = (Z1_1).Phi(); Vphi2 = (Z1_2).Phi();
RooRealVar* theta1 = new RooRealVar("theta1","theta1",Vtheta1);
RooRealVar* phi1 = new RooRealVar("phi1","phi1",Vphi1);
RooRealVar* theta2 = new RooRealVar("theta2","theta2",Vtheta2);
RooRealVar* phi2 = new RooRealVar("phi2","phi2",Vphi2);
// dot product to calculate (p1+p2+ph1+ph2).M()
RooFormulaVar E1("E1","TMath::Sqrt((@0*@0)/((TMath::Sin(@1))*(TMath::Sin(@1)))+@2*@2)",
RooArgList(*pT1,*theta1,*m1));
RooFormulaVar E2("E2","TMath::Sqrt((@0*@0)/((TMath::Sin(@1))*(TMath::Sin(@1)))+@2*@2)",
RooArgList(*pT2,*theta2,*m2));
if(debug_) cout<<"E1 "<<E1.getVal()<<"; E2 "<<E2.getVal()<<endl;
/////
RooRealVar* pTph1 = new RooRealVar("pTph1", "pTph1FIT", RECOpTph1, RECOpTph1min, RECOpTph1+2*pTerrZ1_ph1 );
RooRealVar* pTph2 = new RooRealVar("pTph2", "pTph2FIT", RECOpTph2, RECOpTph2min, RECOpTph2+2*pTerrZ1_ph2 );
double Vthetaph1, Vphiph1, Vthetaph2, Vphiph2;
Vthetaph1 = (Z1_ph1).Theta(); Vthetaph2 = (Z1_ph2).Theta();
Vphiph1 = (Z1_ph1).Phi(); Vphiph2 = (Z1_ph2).Phi();
RooRealVar* thetaph1 = new RooRealVar("thetaph1","thetaph1",Vthetaph1);
RooRealVar* phiph1 = new RooRealVar("phiph1","phiph1",Vphiph1);
RooRealVar* thetaph2 = new RooRealVar("thetaph2","thetaph2",Vthetaph2);
RooRealVar* phiph2 = new RooRealVar("phiph2","phi2",Vphiph2);
RooFormulaVar Eph1("Eph1","TMath::Sqrt((@0*@0)/((TMath::Sin(@1))*(TMath::Sin(@1))))",
RooArgList(*pTph1,*thetaph1));
RooFormulaVar Eph2("Eph2","TMath::Sqrt((@0*@0)/((TMath::Sin(@1))*(TMath::Sin(@1))))",
RooArgList(*pTph2,*thetaph2));
//// dot products of 4-vectors
// 3-vector DOT
RooFormulaVar* p1v3D2 = new RooFormulaVar("p1v3D2",
"@0*@1*( ((TMath::Cos(@2))*(TMath::Cos(@3)))/((TMath::Sin(@2))*(TMath::Sin(@3)))+(TMath::Cos(@4-@5)))",
RooArgList(*pT1,*pT2,*theta1,*theta2,*phi1,*phi2));
if(debug_) cout<<"p1 DOT p2 is "<<p1v3D2->getVal()<<endl;
// 4-vector DOT metric 1 -1 -1 -1
RooFormulaVar p1D2("p1D2","@0*@1-@2",RooArgList(E1,E2,*p1v3D2));
//lep DOT fsrPhoton1
// 3-vector DOT
RooFormulaVar* p1v3Dph1 = new RooFormulaVar("p1v3Dph1",
"@0*@1*( (TMath::Cos(@2)*TMath::Cos(@3))/(TMath::Sin(@2)*TMath::Sin(@3))+TMath::Cos(@4-@5))",
RooArgList(*pT1,*pTph1,*theta1,*thetaph1,*phi1,*phiph1));
// 4-vector DOT metric 1 -1 -1 -1
RooFormulaVar p1Dph1("p1Dph1","@0*@1-@2",RooArgList(E1,Eph1,*p1v3Dph1));
// 3-vector DOT
RooFormulaVar* p2v3Dph1 = new RooFormulaVar("p2v3Dph1",
"@0*@1*( (TMath::Cos(@2)*TMath::Cos(@3))/(TMath::Sin(@2)*TMath::Sin(@3))+TMath::Cos(@4-@5))",
RooArgList(*pT2,*pTph1,*theta2,*thetaph1,*phi2,*phiph1));
// 4-vector DOT metric 1 -1 -1 -1
RooFormulaVar p2Dph1("p2Dph1","@0*@1-@2",RooArgList(E2,Eph1,*p2v3Dph1));
// lep DOT fsrPhoton2
// 3-vector DOT
RooFormulaVar* p1v3Dph2 = new RooFormulaVar("p1v3Dph2",
"@0*@1*( (TMath::Cos(@2)*TMath::Cos(@3))/(TMath::Sin(@2)*TMath::Sin(@3))+TMath::Cos(@4-@5))",
RooArgList(*pT1,*pTph2,*theta1,*thetaph2,*phi1,*phiph2));
// 4-vector DOT metric 1 -1 -1 -1
RooFormulaVar p1Dph2("p1Dph2","@0*@1-@2",RooArgList(E1,Eph2,*p1v3Dph2));
// 3-vector DOT
RooFormulaVar* p2v3Dph2 = new RooFormulaVar("p2v3Dph2",
"@0*@1*( (TMath::Cos(@2)*TMath::Cos(@3))/(TMath::Sin(@2)*TMath::Sin(@3))+TMath::Cos(@4-@5))",
RooArgList(*pT2,*pTph2,*theta2,*thetaph2,*phi2,*phiph2));
// 4-vector DOT metric 1 -1 -1 -1
RooFormulaVar p2Dph2("p2Dph2","@0*@1-@2",RooArgList(E2,Eph2,*p2v3Dph2));
// fsrPhoton1 DOT fsrPhoton2
// 3-vector DOT
RooFormulaVar* ph1v3Dph2 = new RooFormulaVar("ph1v3Dph2",
"@0*@1*( (TMath::Cos(@2)*TMath::Cos(@3))/(TMath::Sin(@2)*TMath::Sin(@3))+TMath::Cos(@4-@5))",
RooArgList(*pTph1,*pTph2,*thetaph1,*thetaph2,*phiph1,*phiph2));
// 4-vector DOT metric 1 -1 -1 -1
RooFormulaVar ph1Dph2("ph1Dph2","@0*@1-@2",RooArgList(Eph1,Eph2,*ph1v3Dph2));
// mZ1
RooFormulaVar* mZ1;
mZ1 = new RooFormulaVar("mZ1","TMath::Sqrt(2*@0+@1*@1+@2*@2)",RooArgList(p1D2,*m1,*m2));
if(p4sZ1ph_.size()==1)
mZ1 = new RooFormulaVar("mZ1","TMath::Sqrt(2*@0+2*@1+2*@2+@3*@3+@4*@4)",
RooArgList(p1D2, p1Dph1, p2Dph1, *m1,*m2));
if(p4sZ1ph_.size()==2)
mZ1 = new RooFormulaVar("mZ1","TMath::Sqrt(2*@0+2*@1+2*@2+2*@3+2*@4+2*@5+@6*@6+@7*@7)",
RooArgList(p1D2,p1Dph1,p2Dph1,p1Dph2,p2Dph2,ph1Dph2, *m1,*m2));
if(debug_) cout<<"mZ1 is "<<mZ1->getVal()<<endl;
// pTerrs, 1,2,ph1,ph2
RooRealVar sigmaZ1_1("sigmaZ1_1", "sigmaZ1_1", pTerrZ1_1);
RooRealVar sigmaZ1_2("sigmaZ1_2", "sigmaZ1_2", pTerrZ1_2);
RooRealVar sigmaZ1_ph1("sigmaZ1_ph1", "sigmaZ1_ph1", pTerrZ1_ph1);
RooRealVar sigmaZ1_ph2("sigmaZ1_ph2", "sigmaZ1_ph2", pTerrZ1_ph2);
// resolution for decay products
RooGaussian gauss1("gauss1","gaussian PDF", *pT1RECO, *pT1, sigmaZ1_1);
RooGaussian gauss2("gauss2","gaussian PDF", *pT2RECO, *pT2, sigmaZ1_2);
RooGaussian gaussph1("gaussph1","gaussian PDF", *pTph1RECO, *pTph1, sigmaZ1_ph1);
RooGaussian gaussph2("gaussph2","gaussian PDF", *pTph2RECO, *pTph2, sigmaZ1_ph2);
RooRealVar bwMean("bwMean", "m_{Z^{0}}", 91.187);
RooRealVar bwGamma("bwGamma", "#Gamma", 2.5);
RooRealVar sg("sg", "sg", sgVal_);
RooRealVar a("a", "a", aVal_);
RooRealVar n("n", "n", nVal_);
RooCBShape CB("CB","CB",*mZ1,bwMean,sg,a,n);
RooRealVar f("f","f", fVal_);
RooRealVar mean("mean","mean",meanVal_);
RooRealVar sigma("sigma","sigma",sigmaVal_);
RooRealVar f1("f1","f1",f1Val_);
RooGenericPdf RelBW("RelBW","1/( pow(mZ1*mZ1-bwMean*bwMean,2)+pow(mZ1,4)*pow(bwGamma/bwMean,2) )", RooArgSet(*mZ1,bwMean,bwGamma) );
RooAddPdf RelBWxCB("RelBWxCB","RelBWxCB", RelBW, CB, f);
RooGaussian gauss("gauss","gauss",*mZ1,mean,sigma);
RooAddPdf RelBWxCBxgauss("RelBWxCBxgauss","RelBWxCBxgauss", RelBWxCB, gauss, f1);
RooProdPdf *PDFRelBWxCBxgauss;
PDFRelBWxCBxgauss = new RooProdPdf("PDFRelBWxCBxgauss","PDFRelBWxCBxgauss",
RooArgList(gauss1, gauss2, RelBWxCBxgauss) );
if(p4sZ1ph_.size()==1)
PDFRelBWxCBxgauss = new RooProdPdf("PDFRelBWxCBxgauss","PDFRelBWxCBxgauss",
RooArgList(gauss1, gauss2, gaussph1, RelBWxCBxgauss) );
if(p4sZ1ph_.size()==2)
PDFRelBWxCBxgauss = new RooProdPdf("PDFRelBWxCBxgauss","PDFRelBWxCBxgauss",
RooArgList(gauss1, gauss2, gaussph1, gaussph2, RelBWxCBxgauss) );
// observable set
RooArgSet *rastmp;
rastmp = new RooArgSet(*pT1RECO,*pT2RECO);
if(p4sZ1ph_.size()==1)
rastmp = new RooArgSet(*pT1RECO,*pT2RECO,*pTph1RECO);
if(p4sZ1ph_.size()>=2)
rastmp = new RooArgSet(*pT1RECO,*pT2RECO,*pTph1RECO,*pTph2RECO);
RooDataSet* pTs = new RooDataSet("pTs","pTs", *rastmp);
pTs->add(*rastmp);
//RooAbsReal* nll;
//nll = PDFRelBWxCBxgauss->createNLL(*pTs);
//RooMinuit(*nll).migrad();
RooFitResult* r = PDFRelBWxCBxgauss->fitTo(*pTs,RooFit::Save(),RooFit::PrintLevel(-1));
const TMatrixDSym& covMatrix = r->covarianceMatrix();
const RooArgList& finalPars = r->floatParsFinal();
for (int i=0 ; i<finalPars.getSize(); i++){
TString name = TString(((RooRealVar*)finalPars.at(i))->GetName());
if(debug_) cout<<"name list of RooRealVar for covariance matrix "<<name<<endl;
}
int size = covMatrix.GetNcols();
//TMatrixDSym covMatrixTest_(size);
covMatrixZ1_.ResizeTo(size,size);
covMatrixZ1_ = covMatrix;
if(debug_) cout<<"save the covariance matrix"<<endl;
l1 = pT1->getVal()/RECOpT1; l2 = pT2->getVal()/RECOpT2;
double pTerrZ1REFIT1 = pT1->getError(); double pTerrZ1REFIT2 = pT2->getError();
pTerrsZ1REFIT_.push_back(pTerrZ1REFIT1);
pTerrsZ1REFIT_.push_back(pTerrZ1REFIT2);
if(p4sZ1ph_.size()>=1){
if(debug_) cout<<"set refit result for Z1 fsr photon 1"<<endl;
lph1 = pTph1->getVal()/RECOpTph1;
double pTerrZ1phREFIT1 = pTph1->getError();
if(debug_) cout<<"scale "<<lph1<<" pterr "<<pTerrZ1phREFIT1<<endl;
pTerrsZ1phREFIT_.push_back(pTerrZ1phREFIT1);
}
if(p4sZ1ph_.size()==2){
lph2 = pTph2->getVal()/RECOpTph2;
double pTerrZ1phREFIT2 = pTph2->getError();
pTerrsZ1phREFIT_.push_back(pTerrZ1phREFIT2);
}
//delete nll;
delete r;
delete mZ1;
delete pT1; delete pT2; delete pTph1; delete pTph2;
delete pT1RECO; delete pT2RECO; delete pTph1RECO; delete pTph2RECO;
delete ph1v3Dph2; delete p1v3Dph1; delete p2v3Dph1; delete p1v3Dph2; delete p2v3Dph2;
delete PDFRelBWxCBxgauss;
delete pTs;
delete rastmp;
if(debug_) cout<<"end Z1 refit"<<endl;
return 0;
}
// internal routine to run the inverter
HypoTestInverterResult *
RooStats::HypoTestInvTool::RunInverter(RooWorkspace * w,
const char * modelSBName, const char * modelBName,
const char * dataName, int type, int testStatType,
bool useCLs, int npoints, double poimin, double poimax,
int ntoys,
bool useNumberCounting,
const char * nuisPriorName ){
std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("StandardHypoTestDemo","Not existing data %s",dataName);
return 0;
}
else
std::cout << "Using data set " << dataName << std::endl;
if (mUseVectorStore) {
RooAbsData::setDefaultStorageType(RooAbsData::Vector);
data->convertToVectorStore() ;
}
// get models from WS
// get the modelConfig out of the file
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
if (!sbModel) {
Error("StandardHypoTestDemo","Not existing ModelConfig %s",modelSBName);
return 0;
}
// check the model
if (!sbModel->GetPdf()) {
Error("StandardHypoTestDemo","Model %s has no pdf ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("StandardHypoTestDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetObservables()) {
Error("StandardHypoTestInvDemo","Model %s has no observables ",modelSBName);
return 0;
}
if (!sbModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName);
sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// case of no systematics
// remove nuisance parameters from model
if (noSystematics) {
const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
if (nuisPar && nuisPar->getSize() > 0) {
std::cout << "StandardHypoTestInvDemo" << " - Switch off all systematics by setting them constant to their initial values" << std::endl;
RooStats::SetAllConstant(*nuisPar);
}
if (bModel) {
const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
if (bnuisPar)
RooStats::SetAllConstant(*bnuisPar);
}
}
if (!bModel || bModel == sbModel) {
Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
bModel = (ModelConfig*) sbModel->Clone();
bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return 0;
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
if (!bModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi and 0 values ",modelBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (var) {
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
Error("StandardHypoTestInvDemo","Model %s has no valid poi",modelBName);
return 0;
}
}
}
// check model has global observables when there are nuisance pdf
// for the hybrid case the globobs are not needed
if (type != 1 ) {
bool hasNuisParam = (sbModel->GetNuisanceParameters() && sbModel->GetNuisanceParameters()->getSize() > 0);
bool hasGlobalObs = (sbModel->GetGlobalObservables() && sbModel->GetGlobalObservables()->getSize() > 0);
if (hasNuisParam && !hasGlobalObs ) {
// try to see if model has nuisance parameters first
RooAbsPdf * constrPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisanceConstraintPdf_sbmodel");
if (constrPdf) {
Warning("StandardHypoTestInvDemo","Model %s has nuisance parameters but no global observables associated",sbModel->GetName());
Warning("StandardHypoTestInvDemo","\tThe effect of the nuisance parameters will not be treated correctly ");
}
}
}
// run first a data fit
const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
RooRealVar *poi = (RooRealVar*)poiSet->first();
std::cout << "StandardHypoTestInvDemo : POI initial value: " << poi->GetName() << " = " << poi->getVal() << std::endl;
// fit the data first (need to use constraint )
TStopwatch tw;
bool doFit = initialFit;
if (testStatType == 0 && initialFit == -1) doFit = false; // case of LEP test statistic
if (type == 3 && initialFit == -1) doFit = false; // case of Asymptoticcalculator with nominal Asimov
double poihat = 0;
if (minimizerType.size()==0) minimizerType = ROOT::Math::MinimizerOptions::DefaultMinimizerType();
else
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(minimizerType.c_str());
Info("StandardHypoTestInvDemo","Using %s as minimizer for computing the test statistic",
ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str() );
if (doFit) {
// do the fit : By doing a fit the POI snapshot (for S+B) is set to the fit value
// and the nuisance parameters nominal values will be set to the fit value.
// This is relevant when using LEP test statistics
Info( "StandardHypoTestInvDemo"," Doing a first fit to the observed data ");
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
RooStats::RemoveConstantParameters(&constrainParams);
tw.Start();
RooFitResult * fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Save(true) );
if (fitres->status() != 0) {
Warning("StandardHypoTestInvDemo","Fit to the model failed - try with strategy 1 and perform first an Hesse computation");
fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(true), Hesse(false),Minimizer(minimizerType.c_str(),"Migrad"), Strategy(1), PrintLevel(mPrintLevel+1), Constrain(constrainParams), Save(true) );
}
if (fitres->status() != 0)
Warning("StandardHypoTestInvDemo"," Fit still failed - continue anyway.....");
poihat = poi->getVal();
std::cout << "StandardHypoTestInvDemo - Best Fit value : " << poi->GetName() << " = "
<< poihat << " +/- " << poi->getError() << std::endl;
std::cout << "Time for fitting : "; tw.Print();
//save best fit value in the poi snapshot
sbModel->SetSnapshot(*sbModel->GetParametersOfInterest());
std::cout << "StandardHypoTestInvo: snapshot of S+B Model " << sbModel->GetName()
<< " is set to the best fit value" << std::endl;
}
// print a message in case of LEP test statistics because it affects result by doing or not doing a fit
if (testStatType == 0) {
if (!doFit)
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit is not done and the TS will use the nuisances at the model value");
else
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit has been done and the TS will use the nuisances at the best fit value");
}
// build test statistics and hypotest calculators for running the inverter
SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());
// null parameters must includes snapshot of poi plus the nuisance values
RooArgSet nullParams(*sbModel->GetSnapshot());
if (sbModel->GetNuisanceParameters()) nullParams.add(*sbModel->GetNuisanceParameters());
if (sbModel->GetSnapshot()) slrts.SetNullParameters(nullParams);
RooArgSet altParams(*bModel->GetSnapshot());
if (bModel->GetNuisanceParameters()) altParams.add(*bModel->GetNuisanceParameters());
if (bModel->GetSnapshot()) slrts.SetAltParameters(altParams);
// ratio of profile likelihood - need to pass snapshot for the alt
RatioOfProfiledLikelihoodsTestStat
ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
ropl.SetSubtractMLE(false);
if (testStatType == 11) ropl.SetSubtractMLE(true);
ropl.SetPrintLevel(mPrintLevel);
ropl.SetMinimizer(minimizerType.c_str());
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3) profll.SetOneSided(true);
if (testStatType == 4) profll.SetSigned(true);
profll.SetMinimizer(minimizerType.c_str());
profll.SetPrintLevel(mPrintLevel);
profll.SetReuseNLL(mOptimize);
slrts.SetReuseNLL(mOptimize);
ropl.SetReuseNLL(mOptimize);
if (mOptimize) {
profll.SetStrategy(0);
ropl.SetStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
}
if (mMaxPoi > 0) poi->setMax(mMaxPoi); // increase limit
MaxLikelihoodEstimateTestStat maxll(*sbModel->GetPdf(),*poi);
NumEventsTestStat nevtts;
AsymptoticCalculator::SetPrintLevel(mPrintLevel);
// create the HypoTest calculator class
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
else if (type == 1) hc = new HybridCalculator(*data, *bModel, *sbModel);
// else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false, mAsimovBins);
// else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true, mAsimovBins); // for using Asimov data generated with nominal values
else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false );
else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true ); // for using Asimov data generated with nominal values
else {
Error("StandardHypoTestInvDemo","Invalid - calculator type = %d supported values are only :\n\t\t\t 0 (Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",type);
return 0;
}
// set the test statistic
TestStatistic * testStat = 0;
if (testStatType == 0) testStat = &slrts;
if (testStatType == 1 || testStatType == 11) testStat = &ropl;
if (testStatType == 2 || testStatType == 3 || testStatType == 4) testStat = &profll;
if (testStatType == 5) testStat = &maxll;
if (testStatType == 6) testStat = &nevtts;
if (testStat == 0) {
Error("StandardHypoTestInvDemo","Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) , 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",testStatType);
return 0;
}
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
if (toymcs && (type == 0 || type == 1) ) {
// look if pdf is number counting or extended
if (sbModel->GetPdf()->canBeExtended() ) {
if (useNumberCounting) Warning("StandardHypoTestInvDemo","Pdf is extended: but number counting flag is set: ignore it ");
}
else {
// for not extended pdf
if (!useNumberCounting ) {
int nEvents = data->numEntries();
Info("StandardHypoTestInvDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents);
toymcs->SetNEventsPerToy(nEvents);
}
else {
Info("StandardHypoTestInvDemo","using a number counting pdf");
toymcs->SetNEventsPerToy(1);
}
}
toymcs->SetTestStatistic(testStat);
if (data->isWeighted() && !mGenerateBinned) {
Info("StandardHypoTestInvDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set mGenerateBinned to true\n",data->numEntries(), data->sumEntries());
}
toymcs->SetGenerateBinned(mGenerateBinned);
toymcs->SetUseMultiGen(mOptimize);
if (mGenerateBinned && sbModel->GetObservables()->getSize() > 2) {
Warning("StandardHypoTestInvDemo","generate binned is activated but the number of ovservable is %d. Too much memory could be needed for allocating all the bins",sbModel->GetObservables()->getSize() );
}
// set the random seed if needed
if (mRandomSeed >= 0) RooRandom::randomGenerator()->SetSeed(mRandomSeed);
}
// specify if need to re-use same toys
if (reuseAltToys) {
hc->UseSameAltToys();
}
if (type == 1) {
HybridCalculator *hhc = dynamic_cast<HybridCalculator*> (hc);
assert(hhc);
hhc->SetToys(ntoys,ntoys/mNToysRatio); // can use less ntoys for b hypothesis
// remove global observables from ModelConfig (this is probably not needed anymore in 5.32)
bModel->SetGlobalObservables(RooArgSet() );
sbModel->SetGlobalObservables(RooArgSet() );
// check for nuisance prior pdf in case of nuisance parameters
if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
// fix for using multigen (does not work in this case)
toymcs->SetUseMultiGen(false);
ToyMCSampler::SetAlwaysUseMultiGen(false);
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestInvDemo","No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
if (bModel->GetPdf() && bModel->GetObservables() )
nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
else
nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
}
if (!nuisPdf ) {
if (bModel->GetPriorPdf()) {
nuisPdf = bModel->GetPriorPdf();
Info("StandardHypoTestInvDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return 0;
}
}
assert(nuisPdf);
Info("StandardHypoTestInvDemo","Using as nuisance Pdf ... " );
nuisPdf->Print();
const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
RooArgSet * np = nuisPdf->getObservables(*nuisParams);
if (np->getSize() == 0) {
Warning("StandardHypoTestInvDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
hhc->ForcePriorNuisanceAlt(*nuisPdf);
hhc->ForcePriorNuisanceNull(*nuisPdf);
}
}
else if (type == 2 || type == 3) {
if (testStatType == 3) ((AsymptoticCalculator*) hc)->SetOneSided(true);
if (testStatType != 2 && testStatType != 3)
Warning("StandardHypoTestInvDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
}
else if (type == 0 || type == 1)
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys/mNToysRatio);
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(0.95);
calc.UseCLs(useCLs);
calc.SetVerbose(true);
// can speed up using proof-lite
if (mUseProof && mNWorkers > 1) {
ProofConfig pc(*w, mNWorkers, "", kFALSE);
toymcs->SetProofConfig(&pc); // enable proof
}
if (npoints > 0) {
if (poimin > poimax) {
// if no min/max given scan between MLE and +4 sigma
poimin = int(poihat);
poimax = int(poihat + 4 * poi->getError());
}
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc.SetFixedScan(npoints,poimin,poimax);
}
else {
//poi->setMax(10*int( (poihat+ 10 *poi->getError() )/10 ) );
std::cout << "Doing an automatic scan in interval : " << poi->getMin() << " , " << poi->getMax() << std::endl;
}
tw.Start();
HypoTestInverterResult * r = calc.GetInterval();
std::cout << "Time to perform limit scan \n";
tw.Print();
if (mRebuild) {
calc.SetCloseProof(1);
tw.Start();
SamplingDistribution * limDist = calc.GetUpperLimitDistribution(true,mNToyToRebuild);
std::cout << "Time to rebuild distributions " << std::endl;
tw.Print();
if (limDist) {
std::cout << "expected up limit " << limDist->InverseCDF(0.5) << " +/- "
<< limDist->InverseCDF(0.16) << " "
<< limDist->InverseCDF(0.84) << "\n";
//update r to a new updated result object containing the rebuilt expected p-values distributions
// (it will not recompute the expected limit)
if (r) delete r; // need to delete previous object since GetInterval will return a cloned copy
r = calc.GetInterval();
}
else
std::cout << "ERROR : failed to re-build distributions " << std::endl;
}
return r;
}
double getSignalContribution(TH1D * h, double &syserr, TString someoptions=""){ //options: (CaC: cut and count without fitting; only stat error)
using namespace std;
using namespace RooFit;
double nSignalOS;
if(someoptions == "CaC"){
nSignalOS = h->Integral();
syserr = sqrt(h->getIntegral);
}
else{
double rangemin=h->GetXaxis()->GetXmin();
double rangemax=h->GetXaxis()->GetXmax();
RooRealVar mass("mass","mass",rangemin,rangemax);
mass.setRange("cutcut",rangemin,rangemax);
RooDataHist roohist("roohist","roohist",mass,Import(*h));
TString fitname=h->GetName();
RooPlot * frame = mass.frame(Title("Fit " + fitname));
roohist.plotOn(frame,MarkerColor(1),MarkerSize(0.9),MarkerStyle(21));
roohist.statOn(frame);
//signal parameter
RooRealVar nsig("nsig","nsig",1000.,0.,10000000.);
// VOIGTIAN FUNCTION
RooRealVar mean("mean","mean",91.2, 20.0,120.0);
RooRealVar width("width","width",5.0, 0.0, 100.0);
RooRealVar sigmaV("sigmaV","sigmaV",5.0, 0.0, 100.0);
RooVoigtian voigt("voigt","voigt",mass,mean,width,sigmaV);
//======// Parameters for CrystalBall
RooRealVar m0("M_{ll}", "Bias", 114., 80, 120,"GeV");
RooRealVar sigma("#sigma_{CB}","Width", 9.2,3.0,10.0);//,"GeV/c^{2}");
RooRealVar cut("#alpha","Cut", 6., 5., 7.);
RooRealVar power("#gamma","Power", 10., 1., 20.);
RooCBShape CrystalBall("CrystalBall", "A Crystal Ball Lineshape", mass, m0,sigma, cut, power);
//======//Parameters for Breit-Wigner Distribution
RooRealVar mRes("M_{ll}", "Z Resonance Mass", 91.2, 89,94);//,"GeV/c^{2}");
RooRealVar Gamma("#Gamma", "#Gamma", 4.0, 1.0,20.0);//,"GeV/c^{2}");
RooBreitWigner BreitWigner("BreitWigner","A Breit-Wigner Distribution",mass,mRes,Gamma);
// SIGNAL MODEL
RooFFTConvPdf ResolutionModel("Convolution","Convolution", mass, BreitWigner, CrystalBall);
//BG model
RooRealVar nbkg("nbkg","nbkg",10.,0.,200000.,"GeV");
RooRealVar bkg_slope("bkg_slope","slope of the background exponential mass PDF",-0.1,0.1);
RooExponential bkgModel("bkgModel","background mass PDF",mass,bkg_slope);
//add sig + bg
RooAddPdf pdfFinal("pdfFinal","pdfFinal",RooArgList(voigt,bkgModel),RooArgList(nsig,nbkg));
RooFitResult *fitResult = pdfFinal.fitTo(roohist, RooFit::Save(true),
RooFit::Extended(true), RooFit::PrintLevel(-1));
fitResult->Print();//"v"); //verbose
pdfFinal.plotOn(frame,LineColor(4));
pdfFinal.plotOn(frame,Components("bkgModel"),LineColor(kRed),LineStyle(kDashed));
pdfFinal.paramOn(frame);
nSignalOS = nsig.getVal();
double signError = nsig.getError();
cout << "\n\n\nsignal contribution: " << nSignalOS << " +- " << signError << " background contribution: " << nbkg.getVal() << endl;
TCanvas c = TCanvas(fitname+" Zmass",fitname + " Zmass",800,400) ;
c.cd() ; gPad->SetLeftMargin(0.15);
frame->Draw();
c.Write();
syserr=signError;
}
return nSignalOS;
}
vector<Double_t*> simFit(bool makeSoupFit_ = false,
const string tnp_ = "etoTauMargLooseNoCracks70",
const string category_ = "tauAntiEMVA",
const string bin_ = "abseta<1.5",
const float binCenter_ = 0.75,
const float binWidth_ = 0.75,
const float xLow_=60,
const float xHigh_=120,
bool SumW2_ = false,
bool verbose_ = true){
vector<Double_t*> out;
//return out;
//TFile *test = new TFile( outFile->GetName(),"UPDATE");
// output file
TFile *test = new TFile( Form("EtoTauPlotsFit_%s_%s_%f.root",tnp_.c_str(),category_.c_str(),binCenter_),"RECREATE");
test->mkdir(Form("bin%f",binCenter_));
TCanvas *c = new TCanvas("fitCanvas",Form("fitCanvas_%s_%s",tnp_.c_str(),bin_.c_str()),10,30,650,600);
c->SetGrid(0,0);
c->SetFillStyle(4000);
c->SetFillColor(10);
c->SetTicky();
c->SetObjectStat(0);
TCanvas *c2 = new TCanvas("fitCanvasTemplate",Form("fitCanvasTemplate_%s_%s",tnp_.c_str(),bin_.c_str()),10,30,650,600);
c2->SetGrid(0,0);
c2->SetFillStyle(4000);
c2->SetFillColor(10);
c2->SetTicky();
c2->SetObjectStat(0);
// input files
TFile fsup("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_soup.root");
TFile fbkg("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_soup_bkg.root");
TFile fsgn("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_soup_sgn.root");
TFile fdat("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_Data.root");
// data from 2iter:
//TFile fdat("/data_CMS/cms/lbianchini/35pb/testNewWriteFromPAT_Data.root");
//********************** signal only tree *************************/
TTree *fullTreeSgn = (TTree*)fsgn.Get((tnp_+"/fitter_tree").c_str());
TH1F* hSall = new TH1F("hSall","",1,0,150);
TH1F* hSPall = new TH1F("hSPall","",1,0,150);
TH1F* hS = new TH1F("hS","",1,0,150);
TH1F* hSP = new TH1F("hSP","",1,0,150);
fullTreeSgn->Draw("mass>>hS",Form("weight*(%s && mass>%f && mass<%f && mcTrue && signalPFChargedHadrCands<1.5)",bin_.c_str(),xLow_,xHigh_));
fullTreeSgn->Draw("mass>>hSall",Form("weight*(%s && mass>%f && mass<%f)",bin_.c_str(),xLow_,xHigh_));
float SGNtrue = hS->Integral();
float SGNall = hSall->Integral();
fullTreeSgn->Draw("mass>>hSP",Form("weight*(%s && %s>0 && mass>%f && mass<%f && mcTrue && signalPFChargedHadrCands<1.5 )",bin_.c_str(),category_.c_str(),xLow_,xHigh_));
fullTreeSgn->Draw("mass>>hSPall",Form("weight*(%s && %s>0 && mass>%f && mass<%f && signalPFChargedHadrCands<1.5 )",bin_.c_str(),category_.c_str(),xLow_,xHigh_));
float SGNtruePass = hSP->Integral();
float SGNallPass = hSPall->Integral();
//********************** background only tree *************************//
TTree *fullTreeBkg = (TTree*)fbkg.Get((tnp_+"/fitter_tree").c_str());
TH1F* hB = new TH1F("hB","",1,0,150);
TH1F* hBP = new TH1F("hBP","",1,0,150);
fullTreeBkg->Draw("mass>>hB",Form("weight*(%s && mass>%f && mass<%f && signalPFChargedHadrCands<1.5 )",bin_.c_str(),xLow_,xHigh_));
float BKG = hB->Integral();
float BKGUnWeighted = hB->GetEntries();
fullTreeBkg->Draw("mass>>hBP",Form("weight*(%s && %s>0 && mass>%f && mass<%f && signalPFChargedHadrCands<1.5 )",bin_.c_str(),category_.c_str(),xLow_,xHigh_));
float BKGPass = hBP->Integral();
float BKGUnWeightedPass = hBP->GetEntries();
float BKGFail = BKG-BKGPass;
cout << "*********** BKGFail " << BKGFail << endl;
//********************** soup tree *************************//
TTree *fullTreeSoup = (TTree*)fsup.Get((tnp_+"/fitter_tree").c_str());
//********************** data tree *************************//
TTree *fullTreeData = (TTree*)fdat.Get((tnp_+"/fitter_tree").c_str());
//********************** workspace ***********************//
RooWorkspace *w = new RooWorkspace("w","w");
// tree variables to be imported
w->factory("mass[30,120]");
w->factory("weight[0,10000]");
w->factory("abseta[0,2.5]");
w->factory("pt[0,200]");
w->factory("mcTrue[0,1]");
w->factory("signalPFChargedHadrCands[0,10]");
w->factory((category_+"[0,1]").c_str());
// background pass pdf for MC
w->factory("RooExponential::McBackgroundPdfP(mass,McCP[0,-10,10])");
// background fail pdf for MC
w->factory("RooExponential::McBackgroundPdfF(mass,McCF[0,-10,10])");
// background pass pdf for Data
w->factory("RooExponential::DataBackgroundPdfP(mass,DataCP[0,-10,10])");
// background fail pdf for Data
w->factory("RooExponential::DataBackgroundPdfF(mass,DataCF[0,-10,10])");
// fit parameters for background
w->factory("McEfficiency[0.04,0,1]");
w->factory("McNumSgn[0,1000000]");
w->factory("McNumBkgP[0,100000]");
w->factory("McNumBkgF[0,100000]");
w->factory("expr::McNumSgnP('McEfficiency*McNumSgn',McEfficiency,McNumSgn)");
w->factory("expr::McNumSgnF('(1-McEfficiency)*McNumSgn',McEfficiency,McNumSgn)");
w->factory("McPassing[pass=1,fail=0]");
// fit parameters for data
w->factory("DataEfficiency[0.1,0,1]");
w->factory("DataNumSgn[0,1000000]");
w->factory("DataNumBkgP[0,1000000]");
w->factory("DataNumBkgF[0,10000]");
w->factory("expr::DataNumSgnP('DataEfficiency*DataNumSgn',DataEfficiency,DataNumSgn)");
w->factory("expr::DataNumSgnF('(1-DataEfficiency)*DataNumSgn',DataEfficiency,DataNumSgn)");
w->factory("DataPassing[pass=1,fail=0]");
RooRealVar *weight = w->var("weight");
RooRealVar *abseta = w->var("abseta");
RooRealVar *pt = w->var("pt");
RooRealVar *mass = w->var("mass");
mass->setRange(xLow_,xHigh_);
RooRealVar *mcTrue = w->var("mcTrue");
RooRealVar *cut = w->var( category_.c_str() );
RooRealVar *signalPFChargedHadrCands = w->var("signalPFChargedHadrCands");
// build the template for the signal pass sample:
RooDataSet templateP("templateP","dataset for signal-pass template", RooArgSet(*mass,*weight,*abseta,*pt,*cut,*mcTrue,*signalPFChargedHadrCands), Import( *fullTreeSgn ), /*WeightVar( *weight ),*/ Cut( Form("(mcTrue && %s>0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()) ) );
// build the template for the signal fail sample:
RooDataSet templateF("templateF","dataset for signal-fail template", RooArgSet(*mass,*weight,*abseta,*pt,*cut,*mcTrue,*signalPFChargedHadrCands), Import( *fullTreeSgn ), /*WeightVar( *weight ),*/ Cut( Form("(mcTrue && %s<0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()) ) );
mass->setBins(24);
RooDataHist templateHistP("templateHistP","",RooArgSet(*mass), templateP, 1.0);
RooHistPdf TemplateSignalPdfP("TemplateSignalPdfP","",RooArgSet(*mass),templateHistP);
w->import(TemplateSignalPdfP);
mass->setBins(24);
RooDataHist templateHistF("templateHistF","",RooArgSet(*mass),templateF,1.0);
RooHistPdf TemplateSignalPdfF("TemplateSignalPdfF","",RooArgSet(*mass),templateHistF);
w->import(TemplateSignalPdfF);
mass->setBins(10000,"fft");
RooPlot* TemplateFrameP = mass->frame(Bins(24),Title("Template passing"));
templateP.plotOn(TemplateFrameP);
w->pdf("TemplateSignalPdfP")->plotOn(TemplateFrameP);
RooPlot* TemplateFrameF = mass->frame(Bins(24),Title("Template failing"));
templateF.plotOn(TemplateFrameF);
w->pdf("TemplateSignalPdfF")->plotOn(TemplateFrameF);
//w->factory("RooFFTConvPdf::McSignalPdfP(mass,TemplateSignalPdfP,RooTruthModel::McResolModP(mass))");
//w->factory("RooFFTConvPdf::McSignalPdfF(mass,TemplateSignalPdfF,RooTruthModel::McResolModF(mass))");
// FOR GREGORY: PROBLEM WHEN TRY TO USE THE PURE TEMPLATE =>
RooHistPdf McSignalPdfP("McSignalPdfP","McSignalPdfP",RooArgSet(*mass),templateHistP);
RooHistPdf McSignalPdfF("McSignalPdfF","McSignalPdfF",RooArgSet(*mass),templateHistF);
w->import(McSignalPdfP);
w->import(McSignalPdfF);
// FOR GREGORY: FOR DATA, CONVOLUTION IS OK =>
w->factory("RooFFTConvPdf::DataSignalPdfP(mass,TemplateSignalPdfP,RooGaussian::DataResolModP(mass,DataMeanResP[0.0,-5.,5.],DataSigmaResP[0.5,0.,10]))");
w->factory("RooFFTConvPdf::DataSignalPdfF(mass,TemplateSignalPdfF,RooGaussian::DataResolModF(mass,DataMeanResF[-5.,-10.,10.],DataSigmaResF[0.5,0.,10]))");
//w->factory("RooCBShape::DataSignalPdfF(mass,DataMeanF[91.2,88,95.],DataSigmaF[3,0.5,8],DataAlfaF[1.8,0.,10],DataNF[1.0,1e-06,10])");
//w->factory("RooFFTConvPdf::DataSignalPdfF(mass,RooVoigtian::DataVoigF(mass,DataMeanF[85,80,95],DataWidthF[2.49],DataSigmaF[3,0.5,10]),RooCBShape::DataResolModF(mass,DataMeanResF[0.5,0.,10.],DataSigmaResF[0.5,0.,10],DataAlphaResF[0.5,0.,10],DataNResF[1.0,1e-06,10]))");
//w->factory("SUM::DataSignalPdfF(fVBP[0.5,0,1]*RooBifurGauss::bifF(mass,DataMeanResF[91.2,80,95],sigmaLF[10,0.5,40],sigmaRF[0.]), RooVoigtian::voigF(mass, DataMeanResF, widthF[2.49], sigmaVoigF[5,0.1,10]) )" );
// composite model pass for MC
w->factory("SUM::McModelP(McNumSgnP*McSignalPdfP,McNumBkgP*McBackgroundPdfP)");
w->factory("SUM::McModelF(McNumSgnF*McSignalPdfF,McNumBkgF*McBackgroundPdfF)");
// composite model pass for data
w->factory("SUM::DataModelP(DataNumSgnP*DataSignalPdfP,DataNumBkgP*DataBackgroundPdfP)");
w->factory("SUM::DataModelF(DataNumSgnF*DataSignalPdfF,DataNumBkgF*DataBackgroundPdfF)");
// simultaneous fir for MC
w->factory("SIMUL::McModel(McPassing,pass=McModelP,fail=McModelF)");
// simultaneous fir for data
w->factory("SIMUL::DataModel(DataPassing,pass=DataModelP,fail=DataModelF)");
w->Print("V");
w->saveSnapshot("clean", w->allVars());
w->loadSnapshot("clean");
/****************** sim fit to soup **************************/
///////////////////////////////////////////////////////////////
TFile *f = new TFile("dummySoup.root","RECREATE");
TTree* cutTreeSoupP = fullTreeSoup->CopyTree(Form("(%s>0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()));
TTree* cutTreeSoupF = fullTreeSoup->CopyTree(Form("(%s<0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()));
RooDataSet McDataP("McDataP","dataset pass for the soup", RooArgSet(*mass), Import( *cutTreeSoupP ) );
RooDataSet McDataF("McDataF","dataset fail for the soup", RooArgSet(*mass), Import( *cutTreeSoupF ) );
RooDataHist McCombData("McCombData","combined data for the soup", RooArgSet(*mass), Index(*(w->cat("McPassing"))), Import("pass", *(McDataP.createHistogram("histoP",*mass)) ), Import("fail",*(McDataF.createHistogram("histoF",*mass)) ) ) ;
RooPlot* McFrameP = 0;
RooPlot* McFrameF = 0;
RooRealVar* McEffFit = 0;
if(makeSoupFit_){
cout << "**************** N bins in mass " << w->var("mass")->getBins() << endl;
RooFitResult* ResMcCombinedFit = w->pdf("McModel")->fitTo(McCombData, Extended(1), Minos(1), Save(1), SumW2Error( SumW2_ ), Range(xLow_,xHigh_), NumCPU(4) /*, ExternalConstraints( *(w->pdf("ConstrainMcNumBkgF")) )*/ );
test->cd(Form("bin%f",binCenter_));
ResMcCombinedFit->Write("McFitResults_Combined");
RooArgSet McFitParam(ResMcCombinedFit->floatParsFinal());
McEffFit = (RooRealVar*)(&McFitParam["McEfficiency"]);
RooRealVar* McNumSigFit = (RooRealVar*)(&McFitParam["McNumSgn"]);
RooRealVar* McNumBkgPFit = (RooRealVar*)(&McFitParam["McNumBkgP"]);
RooRealVar* McNumBkgFFit = (RooRealVar*)(&McFitParam["McNumBkgF"]);
McFrameP = mass->frame(Bins(24),Title("MC: passing sample"));
McCombData.plotOn(McFrameP,Cut("McPassing==McPassing::pass"));
w->pdf("McModel")->plotOn(McFrameP,Slice(*(w->cat("McPassing")),"pass"), ProjWData(*(w->cat("McPassing")),McCombData), LineColor(kBlue),Range(xLow_,xHigh_));
w->pdf("McModel")->plotOn(McFrameP,Slice(*(w->cat("McPassing")),"pass"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McSignalPdfP"), LineColor(kRed),Range(xLow_,xHigh_));
w->pdf("McModel")->plotOn(McFrameP,Slice(*(w->cat("McPassing")),"pass"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McBackgroundPdfP"), LineColor(kGreen),Range(xLow_,xHigh_));
McFrameF = mass->frame(Bins(24),Title("MC: failing sample"));
McCombData.plotOn(McFrameF,Cut("McPassing==McPassing::fail"));
w->pdf("McModel")->plotOn(McFrameF,Slice(*(w->cat("McPassing")),"fail"), ProjWData(*(w->cat("McPassing")),McCombData), LineColor(kBlue),Range(xLow_,xHigh_));
w->pdf("McModel")->plotOn(McFrameF,Slice(*(w->cat("McPassing")),"fail"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McSignalPdfF"), LineColor(kRed),Range(xLow_,xHigh_));
w->pdf("McModel")->plotOn(McFrameF,Slice(*(w->cat("McPassing")),"fail"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McBackgroundPdfF"), LineColor(kGreen),Range(xLow_,xHigh_));
}
///////////////////////////////////////////////////////////////
/****************** sim fit to data **************************/
///////////////////////////////////////////////////////////////
TFile *f2 = new TFile("dummyData.root","RECREATE");
TTree* cutTreeDataP = fullTreeData->CopyTree(Form("(%s>0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()));
TTree* cutTreeDataF = fullTreeData->CopyTree(Form("(%s<0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()));
RooDataSet DataDataP("DataDataP","dataset pass for the soup", RooArgSet(*mass), Import( *cutTreeDataP ) );
RooDataSet DataDataF("DataDataF","dataset fail for the soup", RooArgSet(*mass), Import( *cutTreeDataF ) );
RooDataHist DataCombData("DataCombData","combined data for the soup", RooArgSet(*mass), Index(*(w->cat("DataPassing"))), Import("pass",*(DataDataP.createHistogram("histoDataP",*mass))),Import("fail",*(DataDataF.createHistogram("histoDataF",*mass)))) ;
RooFitResult* ResDataCombinedFit = w->pdf("DataModel")->fitTo(DataCombData, Extended(1), Minos(1), Save(1), SumW2Error( SumW2_ ), Range(xLow_,xHigh_), NumCPU(4));
test->cd(Form("bin%f",binCenter_));
ResDataCombinedFit->Write("DataFitResults_Combined");
RooArgSet DataFitParam(ResDataCombinedFit->floatParsFinal());
RooRealVar* DataEffFit = (RooRealVar*)(&DataFitParam["DataEfficiency"]);
RooRealVar* DataNumSigFit = (RooRealVar*)(&DataFitParam["DataNumSgn"]);
RooRealVar* DataNumBkgPFit = (RooRealVar*)(&DataFitParam["DataNumBkgP"]);
RooRealVar* DataNumBkgFFit = (RooRealVar*)(&DataFitParam["DataNumBkgF"]);
RooPlot* DataFrameP = mass->frame(Bins(24),Title("Data: passing sample"));
DataCombData.plotOn(DataFrameP,Cut("DataPassing==DataPassing::pass"));
w->pdf("DataModel")->plotOn(DataFrameP,Slice(*(w->cat("DataPassing")),"pass"), ProjWData(*(w->cat("DataPassing")),DataCombData), LineColor(kBlue),Range(xLow_,xHigh_));
w->pdf("DataModel")->plotOn(DataFrameP,Slice(*(w->cat("DataPassing")),"pass"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataSignalPdfP"), LineColor(kRed),Range(xLow_,xHigh_));
w->pdf("DataModel")->plotOn(DataFrameP,Slice(*(w->cat("DataPassing")),"pass"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataBackgroundPdfP"), LineColor(kGreen),LineStyle(kDashed),Range(xLow_,xHigh_));
RooPlot* DataFrameF = mass->frame(Bins(24),Title("Data: failing sample"));
DataCombData.plotOn(DataFrameF,Cut("DataPassing==DataPassing::fail"));
w->pdf("DataModel")->plotOn(DataFrameF,Slice(*(w->cat("DataPassing")),"fail"), ProjWData(*(w->cat("DataPassing")),DataCombData), LineColor(kBlue),Range(xLow_,xHigh_));
w->pdf("DataModel")->plotOn(DataFrameF,Slice(*(w->cat("DataPassing")),"fail"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataSignalPdfF"), LineColor(kRed),Range(xLow_,xHigh_));
w->pdf("DataModel")->plotOn(DataFrameF,Slice(*(w->cat("DataPassing")),"fail"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataBackgroundPdfF"), LineColor(kGreen),LineStyle(kDashed),Range(xLow_,xHigh_));
///////////////////////////////////////////////////////////////
if(makeSoupFit_) c->Divide(2,2);
else c->Divide(2,1);
c->cd(1);
DataFrameP->Draw();
c->cd(2);
DataFrameF->Draw();
if(makeSoupFit_){
c->cd(3);
McFrameP->Draw();
c->cd(4);
McFrameF->Draw();
}
c->Draw();
test->cd(Form("bin%f",binCenter_));
c->Write();
c2->Divide(2,1);
c2->cd(1);
TemplateFrameP->Draw();
c2->cd(2);
TemplateFrameF->Draw();
c2->Draw();
test->cd(Form("bin%f",binCenter_));
c2->Write();
// MINOS errors, otherwise HESSE quadratic errors
float McErrorLo = 0;
float McErrorHi = 0;
if(makeSoupFit_){
McErrorLo = McEffFit->getErrorLo()<0 ? McEffFit->getErrorLo() : (-1)*McEffFit->getError();
McErrorHi = McEffFit->getErrorHi()>0 ? McEffFit->getErrorHi() : McEffFit->getError();
}
float DataErrorLo = DataEffFit->getErrorLo()<0 ? DataEffFit->getErrorLo() : (-1)*DataEffFit->getError();
float DataErrorHi = DataEffFit->getErrorHi()>0 ? DataEffFit->getErrorHi() : DataEffFit->getError();
float BinomialError = TMath::Sqrt(SGNtruePass/SGNtrue*(1-SGNtruePass/SGNtrue)/SGNtrue);
Double_t* truthMC = new Double_t[6];
Double_t* tnpMC = new Double_t[6];
Double_t* tnpData = new Double_t[6];
truthMC[0] = binCenter_;
truthMC[1] = binWidth_;
truthMC[2] = binWidth_;
truthMC[3] = SGNtruePass/SGNtrue;
truthMC[4] = BinomialError;
truthMC[5] = BinomialError;
if(makeSoupFit_){
tnpMC[0] = binCenter_;
tnpMC[1] = binWidth_;
tnpMC[2] = binWidth_;
tnpMC[3] = McEffFit->getVal();
tnpMC[4] = (-1)*McErrorLo;
tnpMC[5] = McErrorHi;
}
tnpData[0] = binCenter_;
tnpData[1] = binWidth_;
tnpData[2] = binWidth_;
tnpData[3] = DataEffFit->getVal();
tnpData[4] = (-1)*DataErrorLo;
tnpData[5] = DataErrorHi;
out.push_back(truthMC);
out.push_back(tnpData);
if(makeSoupFit_) out.push_back(tnpMC);
test->Close();
//delete c; delete c2;
if(verbose_) cout << "returning from bin " << bin_ << endl;
return out;
}
void fitWm(const TString outputDir, // output directory
const Double_t lumi, // integrated luminosity (/fb)
const Double_t nsigma=0 // vary MET corrections by n-sigmas (nsigma=0 means nominal correction)
) {
gBenchmark->Start("fitWm");
//--------------------------------------------------------------------------------------------------------------
// Settings
//==============================================================================================================
// MET histogram binning and range
const Int_t NBINS = 50;
const Double_t METMAX = 100;
const Double_t PT_CUT = 25;
const Double_t ETA_CUT = 2.1;
// file format for output plots
const TString format("png");
// recoil correction
RecoilCorrector recoilCorr("../Recoil/ZmmData/fits.root");//, (!) uncomment to perform corrections to recoil from W-MC/Z-MC
//"../Recoil/WmpMC/fits.root",
//"../Recoil/WmmMC/fits.root",
//"../Recoil/ZmmMC/fits.root");
// NNLO boson pT k-factors
TFile nnloCorrFile("/data/blue/ksung/EWKAna/8TeV/Utils/Ratio.root");
TH1D *hNNLOCorr = (TH1D*)nnloCorrFile.Get("RpT_B");
//
// input ntuple file names
//
enum { eData, eWmunu, eEWK, eAntiData, eAntiWmunu, eAntiEWK }; // data type enum
vector<TString> fnamev;
vector<Int_t> typev;
fnamev.push_back("/data/blue/ksung/EWKAna/8TeV/Selection/Wmunu/ntuples/data_select.root"); typev.push_back(eData);
fnamev.push_back("/data/blue/ksung/EWKAna/8TeV/Selection/Wmunu/ntuples/wm_select.root"); typev.push_back(eWmunu);
fnamev.push_back("/data/blue/ksung/EWKAna/8TeV/Selection/Wmunu/ntuples/ewk_select.root"); typev.push_back(eEWK);
fnamev.push_back("/data/blue/ksung/EWKAna/8TeV/Selection/Wmunu/ntuples/top_select.root"); typev.push_back(eEWK);
fnamev.push_back("/data/blue/ksung/EWKAna/8TeV/Selection/AntiWmunu/ntuples/data_select.root"); typev.push_back(eAntiData);
fnamev.push_back("/data/blue/ksung/EWKAna/8TeV/Selection/AntiWmunu/ntuples/wm_select.root"); typev.push_back(eAntiWmunu);
fnamev.push_back("/data/blue/ksung/EWKAna/8TeV/Selection/AntiWmunu/ntuples/ewk_select.root"); typev.push_back(eAntiEWK);
fnamev.push_back("/data/blue/ksung/EWKAna/8TeV/Selection/AntiWmunu/ntuples/top_select.root"); typev.push_back(eAntiEWK);
//--------------------------------------------------------------------------------------------------------------
// Main analysis code
//==============================================================================================================
// Create output directory
gSystem->mkdir(outputDir,kTRUE);
CPlot::sOutDir = outputDir;
//
// Declare MET histograms
//
TH1D *hDataMet = new TH1D("hDataMet","", NBINS,0,METMAX); hDataMet->Sumw2();
TH1D *hDataMetm = new TH1D("hDataMetm","", NBINS,0,METMAX); hDataMetm->Sumw2();
TH1D *hDataMetp = new TH1D("hDataMetp","", NBINS,0,METMAX); hDataMetp->Sumw2();
TH1D *hWmunuMet = new TH1D("hWmunuMet","", NBINS,0,METMAX); hWmunuMet->Sumw2();
TH1D *hWmunuMetp = new TH1D("hWmunuMetp","",NBINS,0,METMAX); hWmunuMetp->Sumw2();
TH1D *hWmunuMetm = new TH1D("hWmunuMetm","",NBINS,0,METMAX); hWmunuMetm->Sumw2();
TH1D *hEWKMet = new TH1D("hEWKMet", "", NBINS,0,METMAX); hEWKMet->Sumw2();
TH1D *hEWKMetp = new TH1D("hEWKMetp", "", NBINS,0,METMAX); hEWKMetp->Sumw2();
TH1D *hEWKMetm = new TH1D("hEWKMetm", "", NBINS,0,METMAX); hEWKMetm->Sumw2();
TH1D *hAntiDataMet = new TH1D("hAntiDataMet","", NBINS,0,METMAX); hAntiDataMet->Sumw2();
TH1D *hAntiDataMetm = new TH1D("hAntiDataMetm","", NBINS,0,METMAX); hAntiDataMetm->Sumw2();
TH1D *hAntiDataMetp = new TH1D("hAntiDataMetp","", NBINS,0,METMAX); hAntiDataMetp->Sumw2();
TH1D *hAntiWmunuMet = new TH1D("hAntiWmunuMet","", NBINS,0,METMAX); hAntiWmunuMet->Sumw2();
TH1D *hAntiWmunuMetp = new TH1D("hAntiWmunuMetp","",NBINS,0,METMAX); hAntiWmunuMetp->Sumw2();
TH1D *hAntiWmunuMetm = new TH1D("hAntiWmunuMetm","",NBINS,0,METMAX); hAntiWmunuMetm->Sumw2();
TH1D *hAntiEWKMet = new TH1D("hAntiEWKMet", "", NBINS,0,METMAX); hAntiEWKMet->Sumw2();
TH1D *hAntiEWKMetp = new TH1D("hAntiEWKMetp", "", NBINS,0,METMAX); hAntiEWKMetp->Sumw2();
TH1D *hAntiEWKMetm = new TH1D("hAntiEWKMetm", "", NBINS,0,METMAX); hAntiEWKMetm->Sumw2();
//
// Declare variables to read in ntuple
//
UInt_t runNum, lumiSec, evtNum;
UInt_t npv, npu;
Float_t genVPt, genVPhi;
Float_t scale1fb;
Float_t met, metPhi, sumEt, mt, u1, u2;
Int_t q;
LorentzVector *lep=0;
Float_t pfChIso, pfGamIso, pfNeuIso;
TFile *infile=0;
TTree *intree=0;
//
// Loop over files
//
for(UInt_t ifile=0; ifile<fnamev.size(); ifile++) {
// Read input file and get the TTrees
cout << "Processing " << fnamev[ifile] << "..." << endl;
infile = new TFile(fnamev[ifile]); assert(infile);
intree = (TTree*)infile->Get("Events"); assert(intree);
intree->SetBranchAddress("runNum", &runNum); // event run number
intree->SetBranchAddress("lumiSec", &lumiSec); // event lumi section
intree->SetBranchAddress("evtNum", &evtNum); // event number
intree->SetBranchAddress("npv", &npv); // number of primary vertices
intree->SetBranchAddress("npu", &npu); // number of in-time PU events (MC)
intree->SetBranchAddress("genVPt", &genVPt); // GEN W boson pT (signal MC)
intree->SetBranchAddress("genVPhi", &genVPhi); // GEN W boson phi (signal MC)
intree->SetBranchAddress("scale1fb", &scale1fb); // event weight per 1/fb (MC)
intree->SetBranchAddress("met", &met); // MET
intree->SetBranchAddress("metPhi", &metPhi); // phi(MET)
intree->SetBranchAddress("sumEt", &sumEt); // Sum ET
intree->SetBranchAddress("mt", &mt); // transverse mass
intree->SetBranchAddress("u1", &u1); // parallel component of recoil
intree->SetBranchAddress("u2", &u2); // perpendicular component of recoil
intree->SetBranchAddress("q", &q); // lepton charge
intree->SetBranchAddress("lep", &lep); // lepton 4-vector
intree->SetBranchAddress("pfChIso", &pfChIso);
intree->SetBranchAddress("pfGamIso", &pfGamIso);
intree->SetBranchAddress("pfNeuIso", &pfNeuIso);
//
// loop over events
//
for(UInt_t ientry=0; ientry<intree->GetEntries(); ientry++) {
intree->GetEntry(ientry);
if(lep->Pt() < PT_CUT) continue;
if(fabs(lep->Eta()) > ETA_CUT) continue;
if( (typev[ifile]==eAntiData || typev[ifile]==eAntiWmunu || typev[ifile]==eAntiEWK) &&
(pfChIso+pfGamIso+pfNeuIso)>0.5*(lep->Pt()) )
continue;
if(typev[ifile]==eData) {
hDataMet->Fill(met);
if(q>0) { hDataMetp->Fill(met); }
else { hDataMetm->Fill(met); }
} else if(typev[ifile]==eAntiData) {
hAntiDataMet->Fill(met);
if(q>0) { hAntiDataMetp->Fill(met); }
else { hAntiDataMetm->Fill(met); }
} else {
Double_t weight = 1;
weight *= scale1fb*lumi;
if(typev[ifile]==eWmunu) {
Double_t corrMet=met, corrMetPhi=metPhi;
// apply recoil corrections to W MC
Double_t lepPt = lep->Pt();
//Double_t lepPt = gRandom->Gaus(lep->Pt(),0.5); // (!) uncomment to apply scale/res corrections to MC
recoilCorr.Correct(corrMet,corrMetPhi,genVPt,genVPhi,lepPt,lep->Phi(),nsigma,q);
Double_t nnlocorr=1;
for(Int_t ibin=1; ibin<=hNNLOCorr->GetNbinsX(); ibin++) {
if(genVPt >= hNNLOCorr->GetBinLowEdge(ibin) &&
genVPt < (hNNLOCorr->GetBinLowEdge(ibin)+hNNLOCorr->GetBinWidth(ibin)))
nnlocorr = hNNLOCorr->GetBinContent(ibin);
}
//weight *= nnlocorr; // (!) uncomment to apply NNLO corrections
hWmunuMet->Fill(corrMet,weight);
if(q>0) { hWmunuMetp->Fill(corrMet,weight); }
else { hWmunuMetm->Fill(corrMet,weight); }
}
if(typev[ifile]==eAntiWmunu) {
Double_t corrMet=met, corrMetPhi=metPhi;
// apply recoil corrections to W MC
Double_t lepPt = lep->Pt();//gRandom->Gaus(lep->Pt(),0.5);
//Double_t lepPt = gRandom->Gaus(lep->Pt(),0.5); // (!) uncomment to apply scale/res corrections to MC
recoilCorr.Correct(corrMet,corrMetPhi,genVPt,genVPhi,lepPt,lep->Phi(),nsigma,q);
Double_t nnlocorr=1;
for(Int_t ibin=1; ibin<=hNNLOCorr->GetNbinsX(); ibin++) {
if(genVPt >= hNNLOCorr->GetBinLowEdge(ibin) &&
genVPt < (hNNLOCorr->GetBinLowEdge(ibin)+hNNLOCorr->GetBinWidth(ibin)))
nnlocorr = hNNLOCorr->GetBinContent(ibin);
}
//weight *= nnlocorr; // (!) uncomment to apply NNLO corrections
hAntiWmunuMet->Fill(corrMet,weight);
if(q>0) { hAntiWmunuMetp->Fill(corrMet,weight); }
else { hAntiWmunuMetm->Fill(corrMet,weight); }
}
if(typev[ifile]==eEWK) {
hEWKMet->Fill(met,weight);
if(q>0) { hEWKMetp->Fill(met,weight); }
else { hEWKMetm->Fill(met,weight); }
}
if(typev[ifile]==eAntiEWK) {
hAntiEWKMet->Fill(met,weight);
if(q>0) { hAntiEWKMetp->Fill(met,weight); }
else { hAntiEWKMetm->Fill(met,weight); }
}
}
}
}
delete infile;
infile=0, intree=0;
//
// Declare fit parameters for signal and background yields
// Note: W signal and EWK+top PDFs are constrained to the ratio described in MC
//
RooRealVar nSig("nSig","nSig",0.7*(hDataMet->Integral()),0,hDataMet->Integral());
RooRealVar nQCD("nQCD","nQCD",0.3*(hDataMet->Integral()),0,hDataMet->Integral());
RooRealVar cewk("cewk","cewk",0.1,0,5) ;
cewk.setVal(hEWKMet->Integral()/hWmunuMet->Integral());
cewk.setConstant(kTRUE);
RooFormulaVar nEWK("nEWK","nEWK","cewk*nSig",RooArgList(nSig,cewk));
RooRealVar nAntiSig("nAntiSig","nAntiSig",0.05*(hAntiDataMet->Integral()),0,hAntiDataMet->Integral());
RooRealVar nAntiQCD("nAntiQCD","nAntiQCD",0.9*(hDataMet->Integral()),0,hDataMet->Integral());
RooRealVar dewk("dewk","dewk",0.1,0,5) ;
dewk.setVal(hAntiEWKMet->Integral()/hAntiWmunuMet->Integral());
dewk.setConstant(kTRUE);
RooFormulaVar nAntiEWK("nAntiEWK","nAntiEWK","dewk*nAntiSig",RooArgList(nAntiSig,dewk));
RooRealVar nSigp("nSigp","nSigp",0.7*(hDataMetp->Integral()),0,hDataMetp->Integral());
RooRealVar nQCDp("nQCDp","nQCDp",0.3*(hDataMetp->Integral()),0,hDataMetp->Integral());
RooRealVar cewkp("cewkp","cewkp",0.1,0,5) ;
cewkp.setVal(hEWKMetp->Integral()/hWmunuMetp->Integral());
cewkp.setConstant(kTRUE);
RooFormulaVar nEWKp("nEWKp","nEWKp","cewkp*nSigp",RooArgList(nSigp,cewkp));
RooRealVar nAntiSigp("nAntiSigp","nAntiSigp",0.05*(hAntiDataMetp->Integral()),0,hAntiDataMetp->Integral());
RooRealVar nAntiQCDp("nAntiQCDp","nAntiQCDp",0.9*(hAntiDataMetp->Integral()),0,hAntiDataMetp->Integral());
RooRealVar dewkp("dewkp","dewkp",0.1,0,5) ;
dewkp.setVal(hAntiEWKMetp->Integral()/hAntiWmunuMetp->Integral());
dewkp.setConstant(kTRUE);
RooFormulaVar nAntiEWKp("nAntiEWKp","nAntiEWKp","dewkp*nAntiSigp",RooArgList(nAntiSigp,dewkp));
RooRealVar nSigm("nSigm","nSigm",0.7*(hDataMetm->Integral()),0,hDataMetm->Integral());
RooRealVar nQCDm("nQCDm","nQCDm",0.3*(hDataMetm->Integral()),0,hDataMetm->Integral());
RooRealVar cewkm("cewkm","cewkm",0.1,0,5) ;
cewkm.setVal(hEWKMetm->Integral()/hWmunuMetm->Integral());
cewkm.setConstant(kTRUE);
RooFormulaVar nEWKm("nEWKm","nEWKm","cewkm*nSigm",RooArgList(nSigm,cewkm));
RooRealVar nAntiSigm("nAntiSigm","nAntiSigm",0.05*(hAntiDataMetm->Integral()),0,hAntiDataMetm->Integral());
RooRealVar nAntiQCDm("nAntiQCDm","nAntiQCDm",0.9*(hAntiDataMetm->Integral()),0,hAntiDataMetm->Integral());
RooRealVar dewkm("dewkm","dewkm",0.1,0,5) ;
dewkm.setVal(hAntiEWKMetm->Integral()/hAntiWmunuMetm->Integral());
dewkm.setConstant(kTRUE);
RooFormulaVar nAntiEWKm("nAntiEWKm","nAntiEWKm","dewkm*nAntiSigm",RooArgList(nAntiSigm,dewkm));
//
// Construct PDFs for fitting
//
RooRealVar pfmet("pfmet","pfmet",0,METMAX);
pfmet.setBins(NBINS);
// Signal PDFs
RooDataHist wmunuMet ("wmunuMET", "wmunuMET", RooArgSet(pfmet),hWmunuMet); RooHistPdf pdfWm ("wm", "wm", pfmet,wmunuMet, 1);
RooDataHist wmunuMetp("wmunuMETp","wmunuMETp",RooArgSet(pfmet),hWmunuMetp); RooHistPdf pdfWmp("wmp","wmp",pfmet,wmunuMetp,1);
RooDataHist wmunuMetm("wmunuMETm","wmunuMETm",RooArgSet(pfmet),hWmunuMetm); RooHistPdf pdfWmm("wmm","wmm",pfmet,wmunuMetm,1);
// EWK+top PDFs
RooDataHist ewkMet ("ewkMET", "ewkMET", RooArgSet(pfmet),hEWKMet); RooHistPdf pdfEWK ("ewk", "ewk", pfmet,ewkMet, 1);
RooDataHist ewkMetp("ewkMETp","ewkMETp",RooArgSet(pfmet),hEWKMetp); RooHistPdf pdfEWKp("ewkp","ewkp",pfmet,ewkMetp,1);
RooDataHist ewkMetm("ewkMETm","ewkMETm",RooArgSet(pfmet),hEWKMetm); RooHistPdf pdfEWKm("ewkm","ewkm",pfmet,ewkMetm,1);
// QCD Pdfs
CPepeModel1 qcd("qcd",pfmet);
CPepeModel1 qcdp("qcdp",pfmet);
CPepeModel1 qcdm("qcdm",pfmet);
// Signal + Background PDFs
RooAddPdf pdfMet ("pdfMet", "pdfMet", RooArgList(pdfWm,pdfEWK,*(qcd.model)), RooArgList(nSig,nEWK,nQCD));
RooAddPdf pdfMetp("pdfMetp","pdfMetp",RooArgList(pdfWmp,pdfEWKp,*(qcdp.model)),RooArgList(nSigp,nEWKp,nQCDp));
RooAddPdf pdfMetm("pdfMetm","pdfMetm",RooArgList(pdfWmm,pdfEWKm,*(qcdm.model)),RooArgList(nSigm,nEWKm,nQCDm));
// Anti-Signal PDFs
RooDataHist awmunuMet ("awmunuMET", "awmunuMET", RooArgSet(pfmet),hAntiWmunuMet); RooHistPdf apdfWm ("awm", "awm", pfmet,awmunuMet, 1);
RooDataHist awmunuMetp("awmunuMETp","awmunuMETp",RooArgSet(pfmet),hAntiWmunuMetp); RooHistPdf apdfWmp("awmp","awmp",pfmet,awmunuMetp,1);
RooDataHist awmunuMetm("awmunuMETm","awmunuMETm",RooArgSet(pfmet),hAntiWmunuMetm); RooHistPdf apdfWmm("awmm","awmm",pfmet,awmunuMetm,1);
// Anti-EWK+top PDFs
RooDataHist aewkMet ("aewkMET", "aewkMET", RooArgSet(pfmet),hAntiEWKMet); RooHistPdf apdfEWK ("aewk", "aewk", pfmet,aewkMet, 1);
RooDataHist aewkMetp("aewkMETp","aewkMETp",RooArgSet(pfmet),hAntiEWKMetp); RooHistPdf apdfEWKp("aewkp","aewkp",pfmet,aewkMetp,1);
RooDataHist aewkMetm("aewkMETm","aewkMETm",RooArgSet(pfmet),hAntiEWKMetm); RooHistPdf apdfEWKm("aewkm","aewkm",pfmet,aewkMetm,1);
// Anti-QCD Pdfs
CPepeModel1 aqcd("aqcd",pfmet,qcd.a1);
CPepeModel1 aqcdp("aqcdp",pfmet,qcdp.a1);
CPepeModel1 aqcdm("aqcdm",pfmet,qcdm.a1);
// Anti-selection PDFs
RooAddPdf apdfMet ("apdfMet", "apdfMet", RooArgList(apdfWm,apdfEWK,*(aqcd.model)), RooArgList(nAntiSig,nAntiEWK,nAntiQCD));
RooAddPdf apdfMetp("apdfMetp","apdfMetp",RooArgList(apdfWmp,apdfEWKp,*(aqcdp.model)),RooArgList(nAntiSigp,nAntiEWKp,nAntiQCDp));
RooAddPdf apdfMetm("apdfMetm","apdfMetm",RooArgList(apdfWmm,apdfEWKm,*(aqcdm.model)),RooArgList(nAntiSigm,nAntiEWKm,nAntiQCDm));
// PDF for simultaneous fit
RooCategory rooCat("rooCat","rooCat");
rooCat.defineType("Select");
rooCat.defineType("Anti");
RooSimultaneous pdfTotal("pdfTotal","pdfTotal",rooCat);
pdfTotal.addPdf(pdfMet, "Select");
pdfTotal.addPdf(apdfMet,"Anti");
RooSimultaneous pdfTotalp("pdfTotalp","pdfTotalp",rooCat);
pdfTotalp.addPdf(pdfMetp, "Select");
pdfTotalp.addPdf(apdfMetp,"Anti");
RooSimultaneous pdfTotalm("pdfTotalm","pdfTotalm",rooCat);
pdfTotalm.addPdf(pdfMetm, "Select");
pdfTotalm.addPdf(apdfMetm,"Anti");
//
// Perform fits
//
RooDataHist dataMet("dataMet", "dataMet", RooArgSet(pfmet), hDataMet);
RooDataHist antiMet("antiMet", "antiMet", RooArgSet(pfmet), hAntiDataMet);
RooDataHist dataTotal("dataTotal","dataTotal", RooArgList(pfmet), Index(rooCat),
Import("Select", dataMet),
Import("Anti", antiMet));
RooFitResult *fitRes = pdfTotal.fitTo(dataTotal,Extended(),Minos(kTRUE),Save(kTRUE));
RooDataHist dataMetp("dataMetp", "dataMetp", RooArgSet(pfmet), hDataMetp);
RooDataHist antiMetp("antiMetp", "antiMetp", RooArgSet(pfmet), hAntiDataMetp);
RooDataHist dataTotalp("dataTotalp","dataTotalp", RooArgList(pfmet), Index(rooCat),
Import("Select", dataMetp),
Import("Anti", antiMetp));
RooFitResult *fitResp = pdfTotalp.fitTo(dataTotalp,Extended(),Minos(kTRUE),Save(kTRUE));
RooDataHist dataMetm("dataMetm", "dataMetm", RooArgSet(pfmet), hDataMetm);
RooDataHist antiMetm("antiMetm", "antiMetm", RooArgSet(pfmet), hAntiDataMetm);
RooDataHist dataTotalm("dataTotalm","dataTotalm", RooArgList(pfmet), Index(rooCat),
Import("Select", dataMetm),
Import("Anti", antiMetm));
RooFitResult *fitResm = pdfTotalm.fitTo(dataTotalm,Extended(),Minos(kTRUE),Save(kTRUE));
//
// Use histogram version of fitted PDFs to make ratio plots
// (Will also use PDF histograms later for Chi^2 and KS tests)
//
TH1D *hPdfMet = (TH1D*)(pdfMet.createHistogram("hPdfMet", pfmet));
hPdfMet->Scale((nSig.getVal()+nEWK.getVal()+nQCD.getVal())/hPdfMet->Integral());
TH1D *hMetDiff = makeDiffHist(hDataMet,hPdfMet,"hMetDiff");
hMetDiff->SetMarkerStyle(kFullCircle);
hMetDiff->SetMarkerSize(0.9);
TH1D *hPdfMetp = (TH1D*)(pdfMetp.createHistogram("hPdfMetp", pfmet));
hPdfMetp->Scale((nSigp.getVal()+nEWKp.getVal()+nQCDp.getVal())/hPdfMetp->Integral());
TH1D *hMetpDiff = makeDiffHist(hDataMetp,hPdfMetp,"hMetpDiff");
hMetpDiff->SetMarkerStyle(kFullCircle);
hMetpDiff->SetMarkerSize(0.9);
TH1D *hPdfMetm = (TH1D*)(pdfMetm.createHistogram("hPdfMetm", pfmet));
hPdfMetm->Scale((nSigm.getVal()+nEWKm.getVal()+nQCDm.getVal())/hPdfMetm->Integral());
TH1D *hMetmDiff = makeDiffHist(hDataMetm,hPdfMetm,"hMetmDiff");
hMetmDiff->SetMarkerStyle(kFullCircle);
hMetmDiff->SetMarkerSize(0.9);
TH1D *hPdfAntiMet = (TH1D*)(apdfMet.createHistogram("hPdfAntiMet", pfmet));
hPdfAntiMet->Scale((nAntiSig.getVal()+nAntiEWK.getVal()+nAntiQCD.getVal())/hPdfAntiMet->Integral());
TH1D *hAntiMetDiff = makeDiffHist(hAntiDataMet,hPdfAntiMet,"hAntiMetDiff");
hAntiMetDiff->SetMarkerStyle(kFullCircle);
hAntiMetDiff->SetMarkerSize(0.9);
TH1D *hPdfAntiMetp = (TH1D*)(apdfMetp.createHistogram("hPdfAntiMetp", pfmet));
hPdfAntiMetp->Scale((nAntiSigp.getVal()+nAntiEWKp.getVal()+nAntiQCDp.getVal())/hPdfAntiMetp->Integral());
TH1D *hAntiMetpDiff = makeDiffHist(hAntiDataMetp,hPdfAntiMetp,"hAntiMetpDiff");
hAntiMetpDiff->SetMarkerStyle(kFullCircle);
hAntiMetpDiff->SetMarkerSize(0.9);
TH1D *hPdfAntiMetm = (TH1D*)(apdfMetm.createHistogram("hPdfAntiMetm", pfmet));
hPdfAntiMetm->Scale((nAntiSigm.getVal()+nAntiEWKm.getVal()+nAntiQCDm.getVal())/hPdfAntiMetm->Integral());
TH1D *hAntiMetmDiff = makeDiffHist(hAntiDataMetm,hPdfAntiMetm,"hAntiMetmDiff");
hAntiMetmDiff->SetMarkerStyle(kFullCircle);
hAntiMetmDiff->SetMarkerSize(0.9);
//--------------------------------------------------------------------------------------------------------------
// Make plots
//==============================================================================================================
TCanvas *c = MakeCanvas("c","c",800,800);
c->Divide(1,2,0,0);
c->cd(1)->SetPad(0,0.3,1.0,1.0);
c->cd(1)->SetTopMargin(0.1);
c->cd(1)->SetBottomMargin(0.01);
c->cd(1)->SetLeftMargin(0.15);
c->cd(1)->SetRightMargin(0.07);
c->cd(1)->SetTickx(1);
c->cd(1)->SetTicky(1);
c->cd(2)->SetPad(0,0,1.0,0.3);
c->cd(2)->SetTopMargin(0.05);
c->cd(2)->SetBottomMargin(0.45);
c->cd(2)->SetLeftMargin(0.15);
c->cd(2)->SetRightMargin(0.07);
c->cd(2)->SetTickx(1);
c->cd(2)->SetTicky(1);
gStyle->SetTitleOffset(1.100,"Y");
TGaxis::SetMaxDigits(3);
char ylabel[100]; // string buffer for y-axis label
// label for lumi
char lumitext[100];
if(lumi<0.1) sprintf(lumitext,"%.1f pb^{-1} at #sqrt{s} = 8 TeV",lumi*1000.);
else sprintf(lumitext,"%.2f fb^{-1} at #sqrt{s} = 8 TeV",lumi);
// plot colors
Int_t linecolorW = kOrange-3;
Int_t fillcolorW = kOrange-2;
Int_t linecolorEWK = kOrange+10;
Int_t fillcolorEWK = kOrange+7;
Int_t linecolorQCD = kViolet+2;
Int_t fillcolorQCD = kViolet-5;
Int_t ratioColor = kGray+2;
//
// Dummy histograms for TLegend
// (I can't figure out how to properly pass RooFit objects...)
//
TH1D *hDummyData = new TH1D("hDummyData","",0,0,10);
hDummyData->SetMarkerStyle(kFullCircle);
hDummyData->SetMarkerSize(0.9);
TH1D *hDummyW = new TH1D("hDummyW","",0,0,10);
hDummyW->SetLineColor(linecolorW);
hDummyW->SetFillColor(fillcolorW);
hDummyW->SetFillStyle(1001);
TH1D *hDummyEWK = new TH1D("hDummyEWK","",0,0,10);
hDummyEWK->SetLineColor(linecolorEWK);
hDummyEWK->SetFillColor(fillcolorEWK);
hDummyEWK->SetFillStyle(1001);
TH1D *hDummyQCD = new TH1D("hDummyQCD","",0,0,10);
hDummyQCD->SetLineColor(linecolorQCD);
hDummyQCD->SetFillColor(fillcolorQCD);
hDummyQCD->SetFillStyle(1001);
//
// W MET plot
//
RooPlot *wmframe = pfmet.frame(Bins(NBINS));
wmframe->GetYaxis()->SetNdivisions(505);
dataMet.plotOn(wmframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
pdfMet.plotOn(wmframe,FillColor(fillcolorW),DrawOption("F"));
pdfMet.plotOn(wmframe,LineColor(linecolorW));
pdfMet.plotOn(wmframe,Components(RooArgSet(pdfEWK,*(qcd.model))),FillColor(fillcolorEWK),DrawOption("F"));
pdfMet.plotOn(wmframe,Components(RooArgSet(pdfEWK,*(qcd.model))),LineColor(linecolorEWK));
pdfMet.plotOn(wmframe,Components(RooArgSet(*(qcd.model))),FillColor(fillcolorQCD),DrawOption("F"));
pdfMet.plotOn(wmframe,Components(RooArgSet(*(qcd.model))),LineColor(linecolorQCD));
pdfMet.plotOn(wmframe,Components(RooArgSet(pdfWm)),LineColor(linecolorW),LineStyle(2));
dataMet.plotOn(wmframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
sprintf(ylabel,"Events / %.1f GeV",hDataMet->GetBinWidth(1));
CPlot plotMet("fitmet",wmframe,"","",ylabel);
plotMet.SetLegend(0.68,0.57,0.93,0.77);
plotMet.GetLegend()->AddEntry(hDummyData,"data","PL");
plotMet.GetLegend()->AddEntry(hDummyW,"W#rightarrow#mu#nu","F");
plotMet.GetLegend()->AddEntry(hDummyEWK,"EWK+t#bar{t}","F");
plotMet.GetLegend()->AddEntry(hDummyQCD,"QCD","F");
plotMet.AddTextBox(lumitext,0.55,0.80,0.90,0.86,0);
plotMet.AddTextBox("CMS Preliminary",0.63,0.92,0.95,0.99,0);
plotMet.SetYRange(0.1,1.1*(hDataMet->GetMaximum()));
plotMet.Draw(c,kFALSE,format,1);
CPlot plotMetDiff("fitmet","","#slash{E}_{T} [GeV]","#chi");
plotMetDiff.AddHist1D(hMetDiff,"EX0",ratioColor);
plotMetDiff.SetYRange(-8,8);
plotMetDiff.AddLine(0, 0,METMAX, 0,kBlack,1);
plotMetDiff.AddLine(0, 5,METMAX, 5,kBlack,3);
plotMetDiff.AddLine(0,-5,METMAX,-5,kBlack,3);
plotMetDiff.Draw(c,kTRUE,format,2);
plotMet.SetName("fitmetlog");
plotMet.SetLogy();
plotMet.SetYRange(1e-3*(hDataMet->GetMaximum()),10*(hDataMet->GetMaximum()));
plotMet.Draw(c,kTRUE,format,1);
RooPlot *awmframe = pfmet.frame(Bins(NBINS));
antiMet.plotOn(awmframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
apdfMet.plotOn(awmframe,FillColor(fillcolorW),DrawOption("F"));
apdfMet.plotOn(awmframe,LineColor(linecolorW));
apdfMet.plotOn(awmframe,Components(RooArgSet(apdfEWK,*(aqcd.model))),FillColor(fillcolorEWK),DrawOption("F"));
apdfMet.plotOn(awmframe,Components(RooArgSet(apdfEWK,*(aqcd.model))),LineColor(linecolorEWK));
apdfMet.plotOn(awmframe,Components(RooArgSet(*(aqcd.model))),FillColor(fillcolorQCD),DrawOption("F"));
apdfMet.plotOn(awmframe,Components(RooArgSet(*(aqcd.model))),LineColor(linecolorQCD));
apdfMet.plotOn(awmframe,Components(RooArgSet(apdfWm)),LineColor(linecolorW),LineStyle(2));
antiMet.plotOn(awmframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
sprintf(ylabel,"Events / %.1f GeV",hAntiDataMet->GetBinWidth(1));
CPlot plotAntiMet("fitantimet",awmframe,"","",ylabel);
plotAntiMet.SetLegend(0.68,0.57,0.93,0.77);
plotAntiMet.GetLegend()->AddEntry(hDummyData,"data","PL");
plotAntiMet.GetLegend()->AddEntry(hDummyW,"W#rightarrow#mu#nu","F");
plotAntiMet.GetLegend()->AddEntry(hDummyEWK,"EWK+t#bar{t}","F");
plotAntiMet.GetLegend()->AddEntry(hDummyQCD,"QCD","F");
plotAntiMet.AddTextBox(lumitext,0.55,0.80,0.90,0.86,0);
plotAntiMet.AddTextBox("CMS Preliminary",0.63,0.92,0.95,0.99,0);
plotAntiMet.SetYRange(0.1,1.1*(hAntiDataMet->GetMaximum()));
plotAntiMet.Draw(c,kFALSE,format,1);
CPlot plotAntiMetDiff("fitantimet","","#slash{E}_{T} [GeV]","#chi");
plotAntiMetDiff.AddHist1D(hMetDiff,"EX0",ratioColor);
plotAntiMetDiff.SetYRange(-8,8);
plotAntiMetDiff.AddLine(0, 0,METMAX, 0,kBlack,1);
plotAntiMetDiff.AddLine(0, 5,METMAX, 5,kBlack,3);
plotAntiMetDiff.AddLine(0,-5,METMAX,-5,kBlack,3);
plotAntiMetDiff.Draw(c,kTRUE,format,2);
plotAntiMet.SetName("fitantimetlog");
plotAntiMet.SetLogy();
plotAntiMet.SetYRange(1e-3*(hAntiDataMet->GetMaximum()),10*(hAntiDataMet->GetMaximum()));
plotAntiMet.Draw(c,kTRUE,format,1);
//
// W+ MET plot
//
RooPlot *wmpframe = pfmet.frame(Bins(NBINS));
wmpframe->GetYaxis()->SetNdivisions(505);
dataMetp.plotOn(wmpframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
pdfMetp.plotOn(wmpframe,FillColor(fillcolorW),DrawOption("F"));
pdfMetp.plotOn(wmpframe,LineColor(linecolorW));
pdfMetp.plotOn(wmpframe,Components(RooArgSet(pdfEWKp,*(qcdp.model))),FillColor(fillcolorEWK),DrawOption("F"));
pdfMetp.plotOn(wmpframe,Components(RooArgSet(pdfEWKp,*(qcdp.model))),LineColor(linecolorEWK));
pdfMetp.plotOn(wmpframe,Components(RooArgSet(*(qcdp.model))),FillColor(fillcolorQCD),DrawOption("F"));
pdfMetp.plotOn(wmpframe,Components(RooArgSet(*(qcdp.model))),LineColor(linecolorQCD));
pdfMetp.plotOn(wmpframe,Components(RooArgSet(pdfWmp)),LineColor(linecolorW),LineStyle(2));
dataMetp.plotOn(wmpframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
sprintf(ylabel,"Events / %.1f GeV",hDataMetp->GetBinWidth(1));
CPlot plotMetp("fitmetp",wmpframe,"","",ylabel);
plotMetp.SetLegend(0.68,0.57,0.93,0.77);
plotMetp.GetLegend()->AddEntry(hDummyData,"data","PL");
plotMetp.GetLegend()->AddEntry(hDummyW,"W^{+}#rightarrow#mu^{+}#nu","F");
plotMetp.GetLegend()->AddEntry(hDummyEWK,"EWK+t#bar{t}","F");
plotMetp.GetLegend()->AddEntry(hDummyQCD,"QCD","F");
plotMetp.AddTextBox(lumitext,0.55,0.80,0.90,0.86,0);
plotMetp.AddTextBox("CMS Preliminary",0.63,0.92,0.95,0.99,0);
// plotMetp.SetYRange(0.1,1.1*(hDataMetp->GetMaximum()));
plotMetp.SetYRange(0.1,4100);
plotMetp.Draw(c,kFALSE,format,1);
CPlot plotMetpDiff("fitmetp","","#slash{E}_{T} [GeV]","#chi");
plotMetpDiff.AddHist1D(hMetpDiff,"EX0",ratioColor);
plotMetpDiff.SetYRange(-8,8);
plotMetpDiff.AddLine(0, 0,METMAX, 0,kBlack,1);
plotMetpDiff.AddLine(0, 5,METMAX, 5,kBlack,3);
plotMetpDiff.AddLine(0,-5,METMAX,-5,kBlack,3);
plotMetpDiff.Draw(c,kTRUE,format,2);
plotMetp.SetName("fitmetplog");
plotMetp.SetLogy();
plotMetp.SetYRange(1e-3*(hDataMetp->GetMaximum()),10*(hDataMetp->GetMaximum()));
plotMetp.Draw(c,kTRUE,format,1);
RooPlot *awmpframe = pfmet.frame(Bins(NBINS));
antiMetp.plotOn(awmpframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
apdfMetp.plotOn(awmpframe,FillColor(fillcolorW),DrawOption("F"));
apdfMetp.plotOn(awmpframe,LineColor(linecolorW));
apdfMetp.plotOn(awmpframe,Components(RooArgSet(apdfEWKp,*(aqcdp.model))),FillColor(fillcolorEWK),DrawOption("F"));
apdfMetp.plotOn(awmpframe,Components(RooArgSet(apdfEWKp,*(aqcdp.model))),LineColor(linecolorEWK));
apdfMetp.plotOn(awmpframe,Components(RooArgSet(*(aqcdp.model))),FillColor(fillcolorQCD),DrawOption("F"));
apdfMetp.plotOn(awmpframe,Components(RooArgSet(*(aqcdp.model))),LineColor(linecolorQCD));
apdfMetp.plotOn(awmpframe,Components(RooArgSet(apdfWmp)),LineColor(linecolorW),LineStyle(2));
antiMetp.plotOn(awmpframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
sprintf(ylabel,"Events / %.1f GeV",hAntiDataMetp->GetBinWidth(1));
CPlot plotAntiMetp("fitantimetp",awmpframe,"","",ylabel);
plotAntiMetp.SetLegend(0.68,0.57,0.93,0.77);
plotAntiMetp.GetLegend()->AddEntry(hDummyData,"data","PL");
plotAntiMetp.GetLegend()->AddEntry(hDummyW,"W^{+}#rightarrow#mu^{+}#nu","F");
plotAntiMetp.GetLegend()->AddEntry(hDummyEWK,"EWK+t#bar{t}","F");
plotAntiMetp.GetLegend()->AddEntry(hDummyQCD,"QCD","F");
plotAntiMetp.AddTextBox(lumitext,0.55,0.80,0.90,0.86,0);
plotAntiMetp.AddTextBox("CMS Preliminary",0.63,0.92,0.95,0.99,0);
// plotAntiMetp.SetYRange(0.1,1.1*(hAntiDataMetp->GetMaximum()));
plotAntiMetp.SetYRange(0.1,1500);
plotAntiMetp.Draw(c,kFALSE,format,1);
CPlot plotAntiMetpDiff("fitantimetp","","#slash{E}_{T} [GeV]","#chi");
plotAntiMetpDiff.AddHist1D(hAntiMetpDiff,"EX0",ratioColor);
plotAntiMetpDiff.SetYRange(-8,8);
plotAntiMetpDiff.AddLine(0, 0,METMAX, 0,kBlack,1);
plotAntiMetpDiff.AddLine(0, 5,METMAX, 5,kBlack,3);
plotAntiMetpDiff.AddLine(0,-5,METMAX,-5,kBlack,3);
plotAntiMetpDiff.Draw(c,kTRUE,format,2);
plotAntiMetp.SetName("fitantimetplog");
plotAntiMetp.SetLogy();
plotAntiMetp.SetYRange(1e-3*(hAntiDataMetp->GetMaximum()),10*(hAntiDataMetp->GetMaximum()));
plotAntiMetp.Draw(c,kTRUE,format,1);
//
// W- MET plot
//
RooPlot *wmmframe = pfmet.frame(Bins(NBINS));
wmmframe->GetYaxis()->SetNdivisions(505);
dataMetm.plotOn(wmmframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
pdfMetm.plotOn(wmmframe,FillColor(fillcolorW),DrawOption("F"));
pdfMetm.plotOn(wmmframe,LineColor(linecolorW));
pdfMetm.plotOn(wmmframe,Components(RooArgSet(pdfEWKm,*(qcdm.model))),FillColor(fillcolorEWK),DrawOption("F"));
pdfMetm.plotOn(wmmframe,Components(RooArgSet(pdfEWKm,*(qcdm.model))),LineColor(linecolorEWK));
pdfMetm.plotOn(wmmframe,Components(RooArgSet(*(qcdm.model))),FillColor(fillcolorQCD),DrawOption("F"));
pdfMetm.plotOn(wmmframe,Components(RooArgSet(*(qcdm.model))),LineColor(linecolorQCD));
pdfMetm.plotOn(wmmframe,Components(RooArgSet(pdfWmm)),LineColor(linecolorW),LineStyle(2));
dataMetm.plotOn(wmmframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
sprintf(ylabel,"Events / %.1f GeV",hDataMetm->GetBinWidth(1));
CPlot plotMetm("fitmetm",wmmframe,"","",ylabel);
plotMetm.SetLegend(0.68,0.57,0.93,0.77);
plotMetm.GetLegend()->AddEntry(hDummyData,"data","PL");
plotMetm.GetLegend()->AddEntry(hDummyW,"W^{-}#rightarrow#mu^{-}#bar{#nu}","F");
plotMetm.GetLegend()->AddEntry(hDummyEWK,"EWK+t#bar{t}","F");
plotMetm.GetLegend()->AddEntry(hDummyQCD,"QCD","F");
plotMetm.AddTextBox(lumitext,0.55,0.80,0.90,0.86,0);
plotMetm.AddTextBox("CMS Preliminary",0.63,0.92,0.95,0.99,0);
// plotMetm.SetYRange(0.1,1.1*(hDataMetm->GetMaximum()));
plotMetm.SetYRange(0.1,4100);
plotMetm.Draw(c,kFALSE,format,1);
CPlot plotMetmDiff("fitmetm","","#slash{E}_{T} [GeV]","#chi");
plotMetmDiff.AddHist1D(hMetmDiff,"EX0",ratioColor);
plotMetmDiff.SetYRange(-8,8);
plotMetmDiff.AddLine(0, 0,METMAX, 0,kBlack,1);
plotMetmDiff.AddLine(0, 5,METMAX, 5,kBlack,3);
plotMetmDiff.AddLine(0,-5,METMAX,-5,kBlack,3);
plotMetmDiff.Draw(c,kTRUE,format,2);
plotMetm.SetName("fitmetmlog");
plotMetm.SetLogy();
plotMetm.SetYRange(1e-3*(hDataMetm->GetMaximum()),10*(hDataMetm->GetMaximum()));
plotMetm.Draw(c,kTRUE,format,1);
RooPlot *awmmframe = pfmet.frame(Bins(NBINS));
antiMetm.plotOn(awmmframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
apdfMetm.plotOn(awmmframe,FillColor(fillcolorW),DrawOption("F"));
apdfMetm.plotOn(awmmframe,LineColor(linecolorW));
apdfMetm.plotOn(awmmframe,Components(RooArgSet(apdfEWKm,*(aqcdm.model))),FillColor(fillcolorEWK),DrawOption("F"));
apdfMetm.plotOn(awmmframe,Components(RooArgSet(apdfEWKm,*(aqcdm.model))),LineColor(linecolorEWK));
apdfMetm.plotOn(awmmframe,Components(RooArgSet(*(aqcdm.model))),FillColor(fillcolorQCD),DrawOption("F"));
apdfMetm.plotOn(awmmframe,Components(RooArgSet(*(aqcdm.model))),LineColor(linecolorQCD));
apdfMetm.plotOn(awmmframe,Components(RooArgSet(apdfWmm)),LineColor(linecolorW),LineStyle(2));
antiMetm.plotOn(awmmframe,MarkerStyle(kFullCircle),MarkerSize(0.9),DrawOption("ZP"));
sprintf(ylabel,"Events / %.1f GeV",hDataMetm->GetBinWidth(1));
CPlot plotAntiMetm("fitantimetm",awmmframe,"","",ylabel);
plotAntiMetm.SetLegend(0.68,0.57,0.93,0.77);
plotAntiMetm.GetLegend()->AddEntry(hDummyData,"data","PL");
plotAntiMetm.GetLegend()->AddEntry(hDummyW,"W^{-}#rightarrow#mu^{-}#bar{#nu}","F");
plotAntiMetm.GetLegend()->AddEntry(hDummyEWK,"EWK+t#bar{t}","F");
plotAntiMetm.GetLegend()->AddEntry(hDummyQCD,"QCD","F");
plotAntiMetm.AddTextBox(lumitext,0.55,0.80,0.90,0.86,0);
plotAntiMetm.AddTextBox("CMS Preliminary",0.63,0.92,0.95,0.99,0);
// plotAntiMetm.SetYRange(0.1,1.1*(hAntiDataMetm->GetMaximum()));
plotAntiMetm.SetYRange(0.1,1500);
plotAntiMetm.Draw(c,kFALSE,format,1);
CPlot plotAntiMetmDiff("fitantimetm","","#slash{E}_{T} [GeV]","#chi");
plotAntiMetmDiff.AddHist1D(hAntiMetmDiff,"EX0",ratioColor);
plotAntiMetmDiff.SetYRange(-8,8);
plotAntiMetmDiff.AddLine(0, 0,METMAX, 0,kBlack,1);
plotAntiMetmDiff.AddLine(0, 5,METMAX, 5,kBlack,3);
plotAntiMetmDiff.AddLine(0,-5,METMAX,-5,kBlack,3);
plotAntiMetmDiff.Draw(c,kTRUE,format,2);
plotAntiMetm.SetName("fitantimetmlog");
plotAntiMetm.SetLogy();
plotAntiMetm.SetYRange(1e-3*(hAntiDataMetm->GetMaximum()),10*(hAntiDataMetm->GetMaximum()));
plotAntiMetm.Draw(c,kTRUE,format,1);
//--------------------------------------------------------------------------------------------------------------
// Output
//==============================================================================================================
cout << "*" << endl;
cout << "* SUMMARY" << endl;
cout << "*--------------------------------------------------" << endl;
//
// Write fit results
//
ofstream txtfile;
char txtfname[100];
ios_base::fmtflags flags;
Double_t chi2prob, chi2ndf;
Double_t ksprob, ksprobpe;
chi2prob = hDataMet->Chi2Test(hPdfMet,"PUW");
chi2ndf = hDataMet->Chi2Test(hPdfMet,"CHI2/NDFUW");
ksprob = hDataMet->KolmogorovTest(hPdfMet);
ksprobpe = hDataMet->KolmogorovTest(hPdfMet,"DX");
sprintf(txtfname,"%s/fitresWm.txt",CPlot::sOutDir.Data());
txtfile.open(txtfname);
assert(txtfile.is_open());
flags = txtfile.flags();
txtfile << setprecision(10);
txtfile << " *** Yields *** " << endl;
txtfile << "Selected: " << hDataMet->Integral() << endl;
txtfile << " Signal: " << nSig.getVal() << " +/- " << nSig.getPropagatedError(*fitRes) << endl;
txtfile << " QCD: " << nQCD.getVal() << " +/- " << nQCD.getPropagatedError(*fitRes) << endl;
txtfile << " Other: " << nEWK.getVal() << " +/- " << nEWK.getPropagatedError(*fitRes) << endl;
txtfile << endl;
txtfile.flags(flags);
fitRes->printStream(txtfile,RooPrintable::kValue,RooPrintable::kVerbose);
txtfile << endl;
printCorrelations(txtfile, fitRes);
txtfile << endl;
printChi2AndKSResults(txtfile, chi2prob, chi2ndf, ksprob, ksprobpe);
txtfile.close();
chi2prob = hDataMetp->Chi2Test(hPdfMetp,"PUW");
chi2ndf = hDataMetp->Chi2Test(hPdfMetp,"CHI2/NDFUW");
ksprob = hDataMetp->KolmogorovTest(hPdfMetp);
ksprobpe = hDataMetp->KolmogorovTest(hPdfMetp,"DX");
sprintf(txtfname,"%s/fitresWmp.txt",CPlot::sOutDir.Data());
txtfile.open(txtfname);
assert(txtfile.is_open());
flags = txtfile.flags();
txtfile << setprecision(10);
txtfile << " *** Yields *** " << endl;
txtfile << "Selected: " << hDataMetp->Integral() << endl;
txtfile << " Signal: " << nSigp.getVal() << " +/- " << nSigp.getPropagatedError(*fitResp) << endl;
txtfile << " QCD: " << nQCDp.getVal() << " +/- " << nQCDp.getPropagatedError(*fitResp) << endl;
txtfile << " Other: " << nEWKp.getVal() << " +/- " << nEWKp.getPropagatedError(*fitResp) << endl;
txtfile << endl;
txtfile.flags(flags);
fitResp->printStream(txtfile,RooPrintable::kValue,RooPrintable::kVerbose);
txtfile << endl;
printCorrelations(txtfile, fitResp);
txtfile << endl;
printChi2AndKSResults(txtfile, chi2prob, chi2ndf, ksprob, ksprobpe);
txtfile.close();
chi2prob = hDataMetm->Chi2Test(hPdfMetm,"PUW");
chi2ndf = hDataMetm->Chi2Test(hPdfMetm,"CHI2/NDFUW");
ksprob = hDataMetm->KolmogorovTest(hPdfMetm);
ksprobpe = hDataMetm->KolmogorovTest(hPdfMetm,"DX");
sprintf(txtfname,"%s/fitresWmm.txt",CPlot::sOutDir.Data());
txtfile.open(txtfname);
assert(txtfile.is_open());
flags = txtfile.flags();
txtfile << setprecision(10);
txtfile << " *** Yields *** " << endl;
txtfile << "Selected: " << hDataMetm->Integral() << endl;
txtfile << " Signal: " << nSigm.getVal() << " +/- " << nSigm.getPropagatedError(*fitResm) << endl;
txtfile << " QCD: " << nQCDm.getVal() << " +/- " << nQCDm.getPropagatedError(*fitResm) << endl;
txtfile << " Other: " << nEWKm.getVal() << " +/- " << nEWKm.getPropagatedError(*fitResm) << endl;
txtfile << endl;
txtfile.flags(flags);
fitResm->printStream(txtfile,RooPrintable::kValue,RooPrintable::kVerbose);
txtfile << endl;
printCorrelations(txtfile, fitResm);
txtfile << endl;
printChi2AndKSResults(txtfile, chi2prob, chi2ndf, ksprob, ksprobpe);
txtfile.close();
makeHTML(outputDir);
cout << endl;
cout << " <> Output saved in " << outputDir << "/" << endl;
cout << endl;
gBenchmark->Show("fitWm");
}
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();
}
}
