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 MakePlots(RooWorkspace* wks) {
// Make plots of the data and the best fit model in two cases:
// first the signal+background case
// second the background-only case.
// get some things out of workspace
RooAbsPdf* model = wks->pdf("model");
RooAbsPdf* sigModel = wks->pdf("sigModel");
RooAbsPdf* zjjModel = wks->pdf("zjjModel");
RooAbsPdf* qcdModel = wks->pdf("qcdModel");
RooRealVar* mu = wks->var("mu");
RooRealVar* invMass = wks->var("invMass");
RooAbsData* data = wks->data("data");
//////////////////////////////////////////////////////////
// Make plots for the Alternate hypothesis, eg. let mu float
mu->setConstant(kFALSE);
model->fitTo(*data,Save(kTRUE),Minos(kFALSE), Hesse(kFALSE),PrintLevel(-1));
//plot sig candidates, full model, and individual componenets
new TCanvas();
RooPlot* frame = invMass->frame() ;
data->plotOn(frame ) ;
model->plotOn(frame) ;
model->plotOn(frame,Components(*sigModel),LineStyle(kDashed), LineColor(kRed)) ;
model->plotOn(frame,Components(*zjjModel),LineStyle(kDashed),LineColor(kBlack)) ;
model->plotOn(frame,Components(*qcdModel),LineStyle(kDashed),LineColor(kGreen)) ;
frame->SetTitle("An example fit to the signal + background model");
frame->Draw() ;
// cdata->SaveAs("alternateFit.gif");
//////////////////////////////////////////////////////////
// Do Fit to the Null hypothesis. Eg. fix mu=0
mu->setVal(0); // set signal fraction to 0
mu->setConstant(kTRUE); // set constant
model->fitTo(*data, Save(kTRUE), Minos(kFALSE), Hesse(kFALSE),PrintLevel(-1));
// plot signal candidates with background model and components
new TCanvas();
RooPlot* xframe2 = invMass->frame() ;
data->plotOn(xframe2, DataError(RooAbsData::SumW2)) ;
model->plotOn(xframe2) ;
model->plotOn(xframe2, Components(*zjjModel),LineStyle(kDashed),LineColor(kBlack)) ;
model->plotOn(xframe2, Components(*qcdModel),LineStyle(kDashed),LineColor(kGreen)) ;
xframe2->SetTitle("An example fit to the background-only model");
xframe2->Draw() ;
// cbkgonly->SaveAs("nullFit.gif");
}
//____________________________________
void DoSPlot(RooWorkspace* ws){
std::cout << "Calculate sWeights" << std::endl;
// get what we need out of the workspace to do the fit
RooAbsPdf* model = ws->pdf("model");
RooRealVar* zYield = ws->var("zYield");
RooRealVar* qcdYield = ws->var("qcdYield");
RooDataSet* data = (RooDataSet*) ws->data("data");
// fit the model to the data.
model->fitTo(*data, Extended() );
// The sPlot technique requires that we fix the parameters
// of the model that are not yields after doing the fit.
RooRealVar* sigmaZ = ws->var("sigmaZ");
RooRealVar* qcdMassDecayConst = ws->var("qcdMassDecayConst");
sigmaZ->setConstant();
qcdMassDecayConst->setConstant();
RooMsgService::instance().setSilentMode(true);
// Now we use the SPlot class to add SWeights to our data set
// based on our model and our yield variables
RooStats::SPlot* sData = new RooStats::SPlot("sData","An SPlot",
*data, model, RooArgList(*zYield,*qcdYield) );
// Check that our weights have the desired properties
std::cout << "Check SWeights:" << std::endl;
std::cout << std::endl << "Yield of Z is "
<< zYield->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("zYield") << std::endl;
std::cout << "Yield of QCD is "
<< qcdYield->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("qcdYield") << std::endl
<< std::endl;
for(Int_t i=0; i < 10; i++)
{
std::cout << "z Weight " << sData->GetSWeight(i,"zYield")
<< " qcd Weight " << sData->GetSWeight(i,"qcdYield")
<< " Total Weight " << sData->GetSumOfEventSWeight(i)
<< std::endl;
}
std::cout << std::endl;
// import this new dataset with sWeights
std::cout << "import new dataset with sWeights" << std::endl;
ws->import(*data, Rename("dataWithSWeights"));
}
RooAbsPdf *MakeModel(RooDataHist *data, RooRealVar *mww, char *name) {
char *modelName = (char*)calloc(50, sizeof(char));
strcat(modelName, name);
strcat(modelName, "model");
char *signalName = (char*)calloc(50, sizeof(char));
char *backgroundName = (char*)calloc(50, sizeof(char));
strcat(signalName, name);
strcat(signalName, "signal");
strcat(backgroundName, name);
strcat(backgroundName, "background");
char *meanName = (char*)calloc(50, sizeof(char));
strcat(meanName, name);
strcat(meanName, "mean");
char *sigmaName = (char*)calloc(50, sizeof(char));
strcat(sigmaName, name);
strcat(sigmaName, "sigma");
char *decayName = (char*)calloc(50, sizeof(char));
strcat(decayName, name);
strcat(decayName, "decay");
char *weightName = (char*)calloc(50, sizeof(char));
strcat(weightName, name);
strcat(weightName, "weight");
RooRealVar *mean = new RooRealVar(meanName, "mean", 0, 10000, "GeV");
RooRealVar *sigma = new RooRealVar(sigmaName, "sigma", 0, 3000, "GeV");
RooGaussian *signal = new RooGaussian(signalName, "signal", *mww, *mean, *sigma);
RooRealVar *decay = new RooRealVar(decayName, "decay", -10, 10, "GeV");
RooExponential *background = new RooExponential(backgroundName, "background", *mww, *decay);
RooRealVar *weight = new RooRealVar("decayweight", "weight", 0, 1.0, "None");
RooAbsPdf *model = new RooAddPdf(modelName, "model", RooArgList(*signal, *background), RooArgList(*weight), kTRUE);
model->fitTo(*data);
mean->setConstant();
sigma->setConstant();
weight->setConstant();
decay->setConstant();
return model;
}
void rf510_wsnamedsets()
{
// C r e a t e m o d e l a n d d a t a s e t
// -----------------------------------------------
RooWorkspace* w = new RooWorkspace("w") ;
fillWorkspace(*w) ;
// Exploit convention encoded in named set "parameters" and "observables"
// to use workspace contents w/o need for introspected
RooAbsPdf* model = w->pdf("model") ;
// Generate data from p.d.f. in given observables
RooDataSet* data = model->generate(*w->set("observables"),1000) ;
// Fit model to data
model->fitTo(*data) ;
// Plot fitted model and data on frame of first (only) observable
RooPlot* frame = ((RooRealVar*)w->set("observables")->first())->frame() ;
data->plotOn(frame) ;
model->plotOn(frame) ;
// Overlay plot with model with reference parameters as stored in snapshots
w->loadSnapshot("reference_fit") ;
model->plotOn(frame,LineColor(kRed)) ;
w->loadSnapshot("reference_fit_bkgonly") ;
model->plotOn(frame,LineColor(kRed),LineStyle(kDashed)) ;
// Draw the frame on the canvas
new TCanvas("rf510_wsnamedsets","rf503_wsnamedsets",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
// Print workspace contents
w->Print() ;
// Workspace will remain in memory after macro finishes
gDirectory->Add(w) ;
}
void MCStudy()
{
string fileName = "h4l_Template_2012_lowmass_unconstrained_new_v00.root";
const char * wsName = "combined"; //"w";
const char * modelSBName = "ModelConfig"; //"modelConfig";
const char * dataName = dataname.c_str();
TFile* file = TFile::Open(fileName);
// get the workspace out of file
RooWorkspace* w = (RooWorkspace*) file->Get(wsName);
if (!wsName) {
cout << "workspace not found" << endl;
return -1.0;
}
// get the modelConfig out of the file
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
// get the data out of file
//RooAbsData* data = w->data(dataName);
// get toy MC data
TFile *fhist = new TFile("../ToyMC/toyMC_ggF125.root");
TH1F *hsum = (TH1F*) fhist->Get("sumtoy1");
RooRealVar m4l("m4l","m4l",120,130);
RooDataHist* data = new RooDataHist("data","data",x,hsum);
// get pdf
RooAbsPdf* model = sbModel->GetPdf();
RooPlot* xframe = m4l.frame();
data->plotOn(xframe);
model->fitTo(*data);
model->plotOn(xframe,LineColor(kRed));
TCanvas *c = new TCanvas("c","c");
xframe->Draw();
}
RooDataSet * getDataAndFrac(TString name, TString q2name, TreeReader * mydata, TCut cut, RooRealVar * MM, double * frac, Str2VarMap jpsiPars, double *outnsig)
{
RooRealVar * cosThetaL = new RooRealVar("cosThetaL","cosThetaL",0.,-1.,1.);
RooRealVar * cosThetaB = new RooRealVar("cosThetaB","cosThetaB",0.,-1.,1.);
TCut massCut = "Lb_MassConsLambda > 5590 && Lb_MassConsLambda < 5650";
Analysis * ana = new Analysis(name+"_mass"+q2name,"Lb",mydata,&cut,MM);
ana->AddVariable("J_psi_1S_MM");
ana->AddVariable(cosThetaL);
ana->AddVariable(cosThetaB);
RooAbsPdf * mysig = stringToPdf("Gauss","sig",MM,jpsiPars);
RooAbsPdf * mybkg = stringToPdf("Exp","bkgM",MM);
RooRealVar * mynsig = new RooRealVar("mynsig","mynsig",50,0,100000);
RooRealVar * mynbkg = new RooRealVar("mynbkg","mynbkg",10,0,100000);
RooAbsPdf * Mmodel = new RooAddPdf("MassModel","MassModel",RooArgSet(*mysig,*mybkg),RooArgSet(*mynsig,*mynbkg));
ana->applyCuts(&cut);
RooDataSet * data = ana->GetDataSet("-recalc");
Mmodel->fitTo(*data,Extended(kTRUE));
double sigBkg = mybkg->createIntegral(*MM,NormSet(*MM),Range("Signal"))->getVal();
double sig = mysig->createIntegral(*MM,NormSet(*MM),Range("Signal"))->getVal();
double nsig = mynsig->getVal();
double nbkg = mynbkg->getVal();
if(frac)
{
frac[0] = nsig*sig/(nsig*sig+nbkg*sigBkg);
frac[1] = frac[0]*TMath::Sqrt( TMath::Power(mynsig->getError()/nsig,2) + TMath::Power(mynbkg->getError()/nbkg,2) );
}
TCut mycut = cut + massCut;
ana->applyCuts(&mycut);
TCanvas * cc = new TCanvas();
GetFrame(MM,Mmodel,data,"-nochi2-plotAllComp",30,NULL,0,"cos#theta_{#Lambda}")->Draw();
cc->Print("M_"+name+"_"+q2name+".pdf");
if(*outnsig) *outnsig = nsig;
return ana->GetDataSet("-recalc");
}
void rf509_wsinteractive()
{
// C r e a t e a n d f i l l w o r k s p a c e
// ------------------------------------------------
// Create a workspace named 'w'
// With CINT w could exports its contents to
// a same-name C++ namespace in CINT 'namespace w'.
// but this does not work anymore in CLING.
// so this tutorial is an example on how to
// change the code
RooWorkspace* w = new RooWorkspace("w",kTRUE) ;
// Fill workspace with p.d.f. and data in a separate function
fillWorkspace(*w) ;
// Print workspace contents
w->Print() ;
// this does not work anymore with CLING
// use normal workspace functionality
// U s e w o r k s p a c e c o n t e n t s
// ----------------------------------------------
// Old syntax to use the name space prefix operator to access the workspace contents
//
//RooDataSet* d = w::model.generate(w::x,1000) ;
//RooFitResult* r = w::model.fitTo(*d) ;
// use normal workspace methods
RooAbsPdf * model = w->pdf("model");
RooRealVar * x = w->var("x");
RooDataSet* d = model->generate(*x,1000) ;
RooFitResult* r = model->fitTo(*d) ;
// old syntax to access the variable x
// RooPlot* frame = w::x.frame() ;
RooPlot* frame = x->frame() ;
d->plotOn(frame) ;
// OLD syntax to ommit x::
// NB: The 'w::' prefix can be omitted if namespace w is imported in local namespace
// in the usual C++ way
//
// using namespace w;
// model.plotOn(frame) ;
// model.plotOn(frame,Components(bkg),LineStyle(kDashed)) ;
// new correct syntax
RooAbsPdf *bkg = w->pdf("bkg");
model->plotOn(frame);
model->plotOn(frame,Components(*bkg),LineStyle(kDashed)) ;
// Draw the frame on the canvas
new TCanvas("rf509_wsinteractive","rf509_wsinteractive",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
}
void FitterUtils::prepare_PDFs(string trigStr, string BDTVar, double BDTcut,
string signalfile, string partrecofile, string combfile, string JpsiLeakfile,
double minBMass, double maxBMass,
string signaltree, string partrecotree, string combtree, string JpsiLeaktree)
{
//***********Get the datasets
TFile* fSignal = new TFile(signalfile.c_str());
TTree* tSignal = (TTree*)fSignal->Get(signaltree.c_str());
TFile* fPartReco = new TFile(partrecofile.c_str());
TTree* tPartReco = (TTree*)fPartReco->Get(partrecotree.c_str());
TFile* fComb = new TFile(combfile.c_str());
TTree* tComb = (TTree*)fComb->Get(combtree.c_str());
TFile* fJpsiLeak = new TFile(JpsiLeakfile.c_str());
TTree* tJpsiLeak = (TTree*)fJpsiLeak->Get(JpsiLeaktree.c_str());
//**********Define variables
RooRealVar trigVar(trigStr.c_str(), trigStr.c_str(), -10, 10);
RooRealVar BDTRooRealVar(BDTVar.c_str(), BDTVar.c_str(), -1,1);
RooRealVar B_plus_M("B_plus_M", "M_{visible}", minBMass, maxBMass, "MeV");
RooRealVar misPT("misPT", "p_{#perp}", 0, 5000, "MeV");
RooRealVar B_plus_DTFM_M_zero("B_plus_DTFM_M_zero", "M_{constr}", 0, 20000, "MeV");
RooRealVar e_plus_BremMultiplicity("e_plus_BremMultiplicity","e_plus_BremMultiplicity", -1,2);
RooRealVar e_minus_BremMultiplicity("e_minus_BremMultiplicity","e_minus_BremMultiplicity", -1,2);
RooRealVar weightPartReco("weightPartReco", "weightPartReco", 0, 10);
RooRealVar weightLeakage("weightLeakage", "weightLeakage", 0, 10);
RooRealVar dataMCWeightee("DataMCWeightee", "DataMCWeightee",0, 30);
//***********Set only variables needed
tSignal->SetBranchStatus("*", 0); tSignal->SetBranchStatus("B_plus_M", 1); tSignal->SetBranchStatus("misPT", 1); tSignal->SetBranchStatus("B_plus_DTFM_M_zero", 1); tSignal->SetBranchStatus(BDTVar.c_str(),1);
tSignal->SetBranchStatus("e_plus_BremMultiplicity", 1); tSignal->SetBranchStatus("e_minus_BremMultiplicity", 1); tSignal->SetBranchStatus(trigStr.c_str()); tSignal->SetBranchStatus("DataMCWeightee",1);
tPartReco->SetBranchStatus("*", 0); tPartReco->SetBranchStatus("B_plus_M", 1); tPartReco->SetBranchStatus("misPT", 1); tPartReco->SetBranchStatus("B_plus_DTFM_M_zero", 1);tPartReco->SetBranchStatus(BDTVar.c_str(),1);
tPartReco->SetBranchStatus("e_plus_BremMultiplicity", 1); tPartReco->SetBranchStatus("e_minus_BremMultiplicity", 1); tPartReco->SetBranchStatus(trigStr.c_str()); tPartReco->SetBranchStatus("weightPartReco",1);
tComb->SetBranchStatus("*", 0); tComb->SetBranchStatus("B_plus_M", 1); tComb->SetBranchStatus("misPT", 1); tComb->SetBranchStatus("B_plus_DTFM_M_zero", 1);tComb->SetBranchStatus(BDTVar.c_str(),1);
tComb->SetBranchStatus("e_plus_BremMultiplicity", 1); tComb->SetBranchStatus("e_minus_BremMultiplicity", 1); tComb->SetBranchStatus(trigStr.c_str());
tJpsiLeak->SetBranchStatus("*", 0); tJpsiLeak->SetBranchStatus("B_plus_M", 1); tJpsiLeak->SetBranchStatus("misPT", 1);
tJpsiLeak->SetBranchStatus("B_plus_DTFM_M_zero", 1);tJpsiLeak->SetBranchStatus(BDTVar.c_str(),1);
tJpsiLeak->SetBranchStatus("e_plus_BremMultiplicity", 1); tJpsiLeak->SetBranchStatus("e_minus_BremMultiplicity", 1); tJpsiLeak->SetBranchStatus(trigStr.c_str());
tJpsiLeak->SetBranchStatus("weightLeakage",1);
//***********Set Binning
RooBinning defaultMBins(floor((maxBMass-minBMass)/(40.)), B_plus_M.getMin(), B_plus_M.getMax() );
RooBinning defaultMisPTBins(floor(40), misPT.getMin(), misPT.getMax());
RooBinning broaderMBins(floor((maxBMass-minBMass)/(80.)), B_plus_M.getMin(), B_plus_M.getMax());
RooBinning broaderMisPTBins(floor(40), misPT.getMin(), misPT.getMax());
B_plus_M.setBinning( defaultMBins);
misPT.setBinning( defaultMisPTBins );
B_plus_M.setBinning( broaderMBins, "broaderBins");
misPT.setBinning( broaderMisPTBins, "broaderBins" );
B_plus_DTFM_M_zero.setBins(100);
RooArgSet argset(BDTRooRealVar, B_plus_DTFM_M_zero, misPT, B_plus_M, trigVar, e_plus_BremMultiplicity, e_minus_BremMultiplicity);
RooArgSet argsetPartReco(BDTRooRealVar, B_plus_DTFM_M_zero, misPT, B_plus_M, trigVar, e_plus_BremMultiplicity, e_minus_BremMultiplicity, weightPartReco);
RooArgSet argsetLeakage(BDTRooRealVar, B_plus_DTFM_M_zero, misPT, B_plus_M, trigVar, e_plus_BremMultiplicity, e_minus_BremMultiplicity, weightLeakage);
RooArgSet argsetSignal(BDTRooRealVar, B_plus_DTFM_M_zero, misPT, B_plus_M, trigVar, e_plus_BremMultiplicity, e_minus_BremMultiplicity, dataMCWeightee);
cout<<"getting the datasets:"<<endl;
RooDataSet* dataSetSignalZeroGamma;
RooDataSet* dataSetSignalOneGamma;
RooDataSet* dataSetSignalTwoGamma;
RooDataSet* dataSetPartReco;
RooDataSet* dataSetJpsiLeak;
RooDataSet* dataSetComb;
TFile* fw(NULL);
string BDTCutString( ("("+BDTVar+">"+d2s(BDTcut)+")").c_str() );
dataSetSignalZeroGamma = new RooDataSet("dataSetSignalZeroGamma", "dataSetSignalZeroGamma", argsetSignal, Import(*tSignal), Cut(( " ("+trigStr+" > 0.9) && "+BDTCutString+" && ((e_plus_BremMultiplicity+e_minus_BremMultiplicity) > -0.5) && ((e_plus_BremMultiplicity+e_minus_BremMultiplicity) < 0.5) && B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str()), WeightVar("DataMCWeightee") );
dataSetSignalOneGamma = new RooDataSet("dataSetSignalOneGamma", "dataSetSignalOneGamma", argsetSignal, Import(*tSignal), Cut(( " ("+trigStr+" > 0.9) && "+BDTCutString+" && ((e_plus_BremMultiplicity+e_minus_BremMultiplicity) > 0.5) && ((e_plus_BremMultiplicity+e_minus_BremMultiplicity) < 1.5) && B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str()), WeightVar("DataMCWeightee") );
dataSetSignalTwoGamma = new RooDataSet("dataSetSignalTwoGamma", "dataSetSignalTwoGamma", argsetSignal, Import(*tSignal), Cut(( " ("+trigStr+" > 0.9) && "+BDTCutString+" && ((e_plus_BremMultiplicity+e_minus_BremMultiplicity) > 1.5) && ((e_plus_BremMultiplicity+e_minus_BremMultiplicity) < 2.5) && B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str()), WeightVar("DataMCWeightee") );
dataSetPartReco = new RooDataSet("dataSetPartReco", "dataSetPartReco", argsetPartReco, Import(*tPartReco),Cut(("("+trigStr+" > 0.9) && "+BDTCutString+ " && B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str()), WeightVar("weightPartReco"));
dataSetJpsiLeak = new RooDataSet("dataSetJpsiLeak", "dataSetJpsiLeak", argsetLeakage, Import(*tJpsiLeak),Cut(("B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str()), WeightVar("weightLeakage"));
// dataSetComb = new RooDataSet("dataSetComb", "dataSetComb", tComb, argset, ("("+trigStr+" > 0.9) && (UBDT3R > "+d2s(BDTcut-0.03)+") && B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str());
dataSetComb = new RooDataSet("dataSetComb", "dataSetComb", tComb, argset, ("("+trigStr+" > 0.9) && "+BDTCutString+" && B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str());
cout<<"Number of zero: "<< dataSetSignalZeroGamma->sumEntries()<<endl;
cout<<"Number of one: "<< dataSetSignalOneGamma->sumEntries()<<endl;
cout<<"Number of two: "<< dataSetSignalTwoGamma->sumEntries()<<endl;
cout<<"Number of PartReco: "<< dataSetPartReco->sumEntries()<<endl;
cout<<"Number of Jpsi leaking:"<< dataSetJpsiLeak->sumEntries()<<endl;
cout<<"Number of combinatorial events:"<< dataSetComb->sumEntries()<<endl;
cout<<"binning the datasets:"<<endl;
RooArgSet argset2(B_plus_M);
if (fit2D) argset2.add(misPT);
RooDataHist dataHistSignalZeroGamma("dataHistSignalZeroGamma", "dataHistSignalZeroGamma", argset2, *dataSetSignalZeroGamma);
RooDataHist dataHistSignalOneGamma("dataHistSignalOneGamma", "dataHistSignalOneGamma", argset2, *dataSetSignalOneGamma);
RooDataHist dataHistSignalTwoGamma("dataHistSignalTwoGamma", "dataHistSignalTwoGamma", argset2, *dataSetSignalTwoGamma);
RooDataHist dataHistComb("dataHistComb", "dataHistComb", argset2, *dataSetComb);
RooDataHist dataHistPartReco("dataHistPartReco", "dataHistPartReco", argset2, *dataSetPartReco);
RooDataHist dataHistJpsiLeak("dataHistJpsiLeak", "dataHistJpsiLeak", argset2, "broaderBins");
dataHistJpsiLeak.add(*dataSetJpsiLeak);
//*************** Compute Error on J/psi leak
double ErrorJpsi(0);
if(dataSetJpsiLeak->sumEntries(("("+trigStr+" > 0.9) && "+BDTCutString+" && B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str()) > 0) ErrorJpsi = 1./sqrt(dataSetJpsiLeak->sumEntries(("("+trigStr+" > 0.9) && "+BDTCutString+" && B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str()));
RooRealVar fractionalErrorJpsiLeak("fractionalErrorJpsiLeak", "fractionalErrorJpsiLeak", ErrorJpsi);
cout<<"JPSI LEAK: "<<dataSetJpsiLeak->sumEntries(("("+trigStr+" > 0.9) && "+BDTCutString+" && B_plus_M > "+d2s(minBMass)+" && B_plus_M < "+d2s(maxBMass)).c_str());
cout<<"JPSI LEAK fractional Error: "<<ErrorJpsi<<endl;
//***************Create 2D histogram estimates from data
cout<<"Preparing the 3 2D histPdf: 1";
// RooArgSet argset2(B_plus_M);
RooHistPdf histPdfSignalZeroGamma("histPdfSignalZeroGamma", "histPdfSignalZeroGamma", argset2, dataHistSignalZeroGamma,2); cout<<" 2";
RooHistPdf histPdfSignalOneGamma("histPdfSignalOneGamma", "histPdfSignalOneGamma", argset2, dataHistSignalOneGamma,2); cout<<" 3";
RooHistPdf histPdfSignalTwoGamma("histPdfSignalTwoGamma", "histPdfSignalTwoGamma", argset2, dataHistSignalTwoGamma,2); cout<<" 4";
RooHistPdf histPdfPartReco("histPdfPartReco", "histPdfPartReco", argset2, dataHistPartReco,2); cout<<" 5";
RooHistPdf histPdfJpsiLeak("histPdfJpsiLeak", "histPdfJpsiLeak", argset2, dataHistJpsiLeak,2); cout<<" 6";
//***************Create combinatorial from fit to data
RooRealVar expoConst("expoConst", "expoConst", -1e-3, -1, 1);
RooRealVar T("T", "T", 97, 0, 200);
RooRealVar n("n", "n", 3.5, 1., 5.5);
RooAbsPdf *combPDF;
if (fit2D)
{
combPDF = new RooPTMVis("PTMVis", "PTMVis", misPT, B_plus_M, T, n, expoConst);
}
else
{
combPDF = new RooExponential("histPdfComb", "histPdfComb", B_plus_M, expoConst);
}
combPDF->fitTo(*dataSetComb); //
if (fit2D)
{
T.setConstant(true);
n.setConstant(true);
std::cout<<"T generated is: "<<T.getVal()<<std::endl;
}
RooRealVar trueExp("trueExp","trueExp", expoConst.getVal(), -1000, 1000);
trueExp.setError(expoConst.getError());
RooRealVar trueT("trueT","trueT", T.getVal(), -1000, 1000);
trueT.setError(T.getError());
RooRealVar trueN("trueN","trueN", n.getVal(), -1000, 1000);
trueN.setError(n.getError());
//***************Save everything on a workspace
RooWorkspace workspace("workspace", "workspace");
workspace.import(B_plus_DTFM_M_zero);
workspace.import(B_plus_M);
workspace.import(misPT);
workspace.import(expoConst);
workspace.import(trueExp);
workspace.import(T);
workspace.import(n);
workspace.import(*dataSetSignalZeroGamma);
workspace.import(*dataSetSignalOneGamma);
workspace.import(*dataSetSignalTwoGamma);
workspace.import(*dataSetPartReco);
workspace.import(*dataSetComb);
workspace.import(*dataSetJpsiLeak);
workspace.import(histPdfSignalZeroGamma);
workspace.import(histPdfSignalOneGamma);
workspace.import(histPdfSignalTwoGamma);
workspace.import(histPdfPartReco);
workspace.import(histPdfJpsiLeak);
workspace.import(fractionalErrorJpsiLeak);
workspace.import(trueT);
workspace.import(trueN);
workspace.writeToFile(workspacename.c_str());
delete fComb;
delete fSignal;
delete fPartReco;
if(fw!=0 && fw != NULL) delete fw;
delete combPDF;
delete dataSetSignalZeroGamma;
delete dataSetSignalOneGamma;
delete dataSetSignalTwoGamma;
delete dataSetPartReco;
delete dataSetJpsiLeak;
delete dataSetComb;
}
float ComputeTestStat(TString wsfile, double mu_susy_sig_val) {
gROOT->Reset();
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
modelConfig->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
RooRealVar* rrv_mu_susy_sig = ws->var("mu_susy_all0lep") ;
if ( rrv_mu_susy_sig == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_all0lep in workspace. Quitting.\n\n\n") ;
return ;
} else {
printf(" current value is : %8.3f\n", rrv_mu_susy_sig->getVal() ) ; cout << flush ;
rrv_mu_susy_sig->setConstant(kFALSE) ;
}
/*
// check the impact of varying the qcd normalization:
RooRealVar *rrv_qcd_0lepLDP_ratioH1 = ws->var("qcd_0lepLDP_ratio_H1");
RooRealVar *rrv_qcd_0lepLDP_ratioH2 = ws->var("qcd_0lepLDP_ratio_H2");
RooRealVar *rrv_qcd_0lepLDP_ratioH3 = ws->var("qcd_0lepLDP_ratio_H3");
rrv_qcd_0lepLDP_ratioH1->setVal(0.3);
rrv_qcd_0lepLDP_ratioH2->setVal(0.3);
rrv_qcd_0lepLDP_ratioH3->setVal(0.3);
rrv_qcd_0lepLDP_ratioH1->setConstant(kTRUE);
rrv_qcd_0lepLDP_ratioH2->setConstant(kTRUE);
rrv_qcd_0lepLDP_ratioH3->setConstant(kTRUE);
*/
printf("\n\n\n ===== Doing a fit with SUSY component floating ====================\n\n") ;
RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0) ) ;
double logLikelihoodSusyFloat = fitResult->minNll() ;
double logLikelihoodSusyFixed(0.) ;
double testStatVal(-1.) ;
if ( mu_susy_sig_val >= 0. ) {
printf("\n\n\n ===== Doing a fit with SUSY fixed ====================\n\n") ;
printf(" fixing mu_susy_sig to %8.2f.\n", mu_susy_sig_val ) ;
rrv_mu_susy_sig->setVal( mu_susy_sig_val ) ;
rrv_mu_susy_sig->setConstant(kTRUE) ;
fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0) ) ;
logLikelihoodSusyFixed = fitResult->minNll() ;
testStatVal = 2.*(logLikelihoodSusyFixed - logLikelihoodSusyFloat) ;
printf("\n\n\n ======= test statistic : -2 * ln (L_fixed / ln L_max) = %8.3f\n\n\n", testStatVal ) ;
}
return testStatVal ;
}
void ws_profile_interval1( const char* wsfile = "ws-test1.root", const char* parName = "mu_susy_sig", double alpha = 0.10, double mu_susy_sig_val = 0., double xmax = -1. ) {
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
//// ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
//// printf("\n\n\n ===== SbModel ====================\n\n") ;
//// modelConfig->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
printf("\n\n\n ===== RooDataSet ====================\n\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
printf("\n\n\n ===== Grabbing %s rrv ====================\n\n", parName ) ;
RooRealVar* rrv_par = ws->var( parName ) ;
if ( rrv_par == 0x0 ) {
printf("\n\n\n *** can't find %s in workspace. Quitting.\n\n\n", parName ) ; return ;
} else {
printf(" current value is : %8.3f\n", rrv_par->getVal() ) ; cout << flush ;
}
if ( xmax > 0 ) { rrv_par->setMax( xmax ) ; }
printf("\n\n\n ===== Grabbing mu_susy_sig rrv ====================\n\n") ;
RooRealVar* rrv_mu_susy_sig = ws->var("mu_susy_sig") ;
if ( rrv_mu_susy_sig == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_sig in workspace. Quitting.\n\n\n") ; return ;
}
if ( strcmp( parName, "mu_susy_sig" ) != 0 ) {
if ( mu_susy_sig_val >= 0. ) {
printf(" current value is : %8.3f\n", rrv_mu_susy_sig->getVal() ) ; cout << flush ;
printf(" fixing to %8.2f.\n", mu_susy_sig_val ) ;
rrv_mu_susy_sig->setVal( mu_susy_sig_val ) ;
rrv_mu_susy_sig->setConstant(kTRUE) ;
} else {
printf(" current value is : %8.3f\n", rrv_mu_susy_sig->getVal() ) ; cout << flush ;
printf(" allowing mu_susy_sig to float.\n") ;
rrv_mu_susy_sig->setConstant(kFALSE) ;
}
} else {
printf("\n\n profile plot parameter is mu_susy_sig.\n") ;
rrv_mu_susy_sig->setConstant(kFALSE) ;
}
printf("\n\n\n ===== Grabbing likelihood pdf ====================\n\n") ;
RooAbsPdf* likelihood = ws->pdf("likelihood") ;
if ( likelihood == 0x0 ) {
printf("\n\n\n *** can't find likelihood pdf in workspace. Quitting.\n\n\n") ; return ;
} else {
printf("\n\n likelihood pdf: \n\n") ;
likelihood->Print() ;
}
printf("\n\n\n ===== Doing a fit ====================\n\n") ;
likelihood->fitTo( *rds ) ;
double mlValue = rrv_par->getVal() ;
printf(" Maximum likelihood value of %s : %8.3f +/- %8.3f\n",
parName, rrv_par->getVal(), rrv_par->getError() ) ;
printf("\n\n ========== Creating ProfileLikelihoodCalculator\n\n" ) ; cout << flush ;
// ProfileLikelihoodCalculator plc( *rds, *modelConfig ) ;
ProfileLikelihoodCalculator plc( *rds, *likelihood, RooArgSet( *rrv_par ) ) ;
plc.SetTestSize( alpha ) ;
ConfInterval* plinterval = plc.GetInterval() ;
double low = ((LikelihoodInterval*) plinterval)->LowerLimit(*rrv_par) ;
double high = ((LikelihoodInterval*) plinterval)->UpperLimit(*rrv_par) ;
printf("\n\n Limits: %8.3f, %8.3f\n\n", low, high ) ;
printf("\n\n ========= Making profile likelihood plot\n\n") ; cout << flush ;
LikelihoodIntervalPlot* profPlot = new LikelihoodIntervalPlot((LikelihoodInterval*)plinterval) ;
TCanvas* cplplot = new TCanvas("cplplot","cplplot", 500, 400) ;
profPlot->Draw() ;
gPad->SetGridy(1) ;
char plotname[10000] ;
sprintf( plotname, "plplot-%s.png", parName ) ;
cplplot->SaveAs( plotname ) ;
if ( alpha > 0.3 ) {
printf("\n\n\n 1 standard-deviation errors for %s : %8.2f + %8.2f - %8.2f\n\n\n",
parName, mlValue, high-mlValue, mlValue-low ) ;
}
}
void asimov() {
std::string InputFile = "./data/example.root";
// Create the measurement
Measurement meas("meas", "meas");
meas.SetOutputFilePrefix( "./results/asimov_UsingC" );
meas.SetPOI( "SigXsecOverSM" );
meas.AddConstantParam("alpha_syst1");
meas.AddConstantParam("Lumi");
meas.SetLumi( 1.0 );
meas.SetLumiRelErr( 0.10 );
meas.SetExportOnly( false );
meas.SetBinHigh( 2 );
// Create a channel
Channel chan( "channel1" );
chan.SetData( "data", InputFile );
chan.SetStatErrorConfig( 0.05, "Poisson" );
// Now, create some samples
// Create the signal sample
Sample signal( "signal", "signal", InputFile );
signal.AddOverallSys( "syst1", 0.95, 1.05 );
signal.AddNormFactor( "SigXsecOverSM", 1, 0, 3 );
chan.AddSample( signal );
// Background 1
Sample background1( "background1", "background1", InputFile );
background1.ActivateStatError( "background1_statUncert", InputFile );
background1.AddOverallSys( "syst2", 0.95, 1.05 );
chan.AddSample( background1 );
// Background 1
Sample background2( "background2", "background2", InputFile );
background2.ActivateStatError();
background2.AddOverallSys( "syst3", 0.95, 1.05 );
chan.AddSample( background2 );
// Done with this channel
// Add it to the measurement:
meas.AddChannel( chan );
// Collect the histograms from their files,
// print some output,
meas.CollectHistograms();
//meas.PrintTree();
// One can print XML code to an
// output directory:
// meas.PrintXML( "xmlFromCCode", meas.GetOutputFilePrefix() );
RooWorkspace* wspace = RooStats::HistFactory::HistoToWorkspaceFactoryFast::MakeCombinedModel(meas);
// Get the pdf, obs
RooAbsPdf* pdf = wspace->pdf("simPdf");
RooDataSet* obsData = (RooDataSet*) wspace->data("obsData");
RooDataSet* asimovData = (RooDataSet*) wspace->data("asimovData");
// fit the pdf to the asimov data
std::cout << "Fitting to Observed Data" << std::endl;
pdf->fitTo(*obsData);
std::cout << "Fitting to Asimov Data" << std::endl;
pdf->fitTo(*asimovData);
return;
}
void rs603_HLFactoryElaborateExample() {
// --- Prepare the 2 needed datacards for this example ---
TString card_name("rs603_card_WsMaker.rs");
ofstream ofile(card_name);
ofile << "// The simplest card for combination\n\n";
ofile << "gauss1 = Gaussian(x[0,100],mean1[50,0,100],4);\n";
ofile << "flat1 = Polynomial(x,0);\n";
ofile << "sb_model1 = SUM(nsig1[120,0,300]*gauss1 , nbkg1[100,0,1000]*flat1);\n\n";
ofile << "echo In the middle!;\n\n";
ofile << "gauss2 = Gaussian(x,mean2[80,0,100],5);\n";
ofile << "flat2 = Polynomial(x,0);\n";
ofile << "sb_model2 = SUM(nsig2[90,0,400]*gauss2 , nbkg2[80,0,1000]*flat2);\n\n";
ofile << "echo At the end!;\n";
ofile.close();
TString card_name2("rs603_card.rs");
ofstream ofile2(card_name2);
ofile2 << "// The simplest card for combination\n\n";
ofile2 << "gauss1 = Gaussian(x[0,100],mean1[50,0,100],4);\n";
ofile2 << "flat1 = Polynomial(x,0);\n";
ofile2 << "sb_model1 = SUM(nsig1[120,0,300]*gauss1 , nbkg1[100,0,1000]*flat1);\n\n";
ofile2 << "echo In the middle!;\n\n";
ofile2 << "gauss2 = Gaussian(x,mean2[80,0,100],5);\n";
ofile2 << "flat2 = Polynomial(x,0);\n";
ofile2 << "sb_model2 = SUM(nsig2[90,0,400]*gauss2 , nbkg2[80,0,1000]*flat2);\n\n";
ofile2 << "#include rs603_included_card.rs;\n\n";
ofile2 << "echo At the end!;\n";
ofile2.close();
TString card_name3("rs603_included_card.rs");
ofstream ofile3(card_name3);
ofile3 << "echo Now reading the included file!;\n\n";
ofile3 << "echo Including datasets in a Workspace in a Root file...;\n";
ofile3 << "data1 = import(rs603_infile.root,\n";
ofile3 << " rs603_ws,\n";
ofile3 << " data1);\n\n";
ofile3 << "data2 = import(rs603_infile.root,\n";
ofile3 << " rs603_ws,\n";
ofile3 << " data2);\n";
ofile3.close();
// --- Produce the two separate datasets into a WorkSpace ---
HLFactory hlf("HLFactoryComplexExample",
"rs603_card_WsMaker.rs",
false);
RooRealVar* x = static_cast<RooRealVar*>(hlf.GetWs()->arg("x"));
RooAbsPdf* pdf1 = hlf.GetWs()->pdf("sb_model1");
RooAbsPdf* pdf2 = hlf.GetWs()->pdf("sb_model2");
RooWorkspace w("rs603_ws");
RooDataSet* data1 = pdf1->generate(RooArgSet(*x),Extended());
data1->SetName("data1");
w.import(*data1);
RooDataSet* data2 = pdf2->generate(RooArgSet(*x),Extended());
data2->SetName("data2");
w.import(*data2);
// --- Write the WorkSpace into a rootfile ---
TFile outfile("rs603_infile.root","RECREATE");
w.Write();
outfile.Close();
cout << "-------------------------------------------------------------------\n"
<< " Rootfile and Workspace prepared \n"
<< "-------------------------------------------------------------------\n";
HLFactory hlf_2("HLFactoryElaborateExample",
"rs603_card.rs",
false);
x = hlf_2.GetWs()->var("x");
pdf1 = hlf_2.GetWs()->pdf("sb_model1");
pdf2 = hlf_2.GetWs()->pdf("sb_model2");
hlf_2.AddChannel("model1","sb_model1","flat1","data1");
hlf_2.AddChannel("model2","sb_model2","flat2","data2");
RooDataSet* data = hlf_2.GetTotDataSet();
RooAbsPdf* pdf = hlf_2.GetTotSigBkgPdf();
RooCategory* thecat = hlf_2.GetTotCategory();
// --- Perform extended ML fit of composite PDF to toy data ---
pdf->fitTo(*data) ;
// --- Plot toy data and composite PDF overlaid ---
RooPlot* xframe = x->frame() ;
data->plotOn(xframe);
thecat->setIndex(0);
pdf->plotOn(xframe,Slice(*thecat),ProjWData(*thecat,*data)) ;
thecat->setIndex(1);
pdf->plotOn(xframe,Slice(*thecat),ProjWData(*thecat,*data)) ;
gROOT->SetStyle("Plain");
xframe->Draw();
}
void rf105_funcbinding()
{
// B i n d T M a t h : : E r f C f u n c t i o n
// ---------------------------------------------------
// Bind one-dimensional TMath::Erf function as RooAbsReal function
RooRealVar x("x","x",-3,3) ;
RooAbsReal* errorFunc = bindFunction("erf",TMath::Erf,x) ;
// Print erf definition
errorFunc->Print() ;
// Plot erf on frame
RooPlot* frame1 = x.frame(Title("TMath::Erf bound as RooFit function")) ;
errorFunc->plotOn(frame1) ;
// B i n d R O O T : : M a t h : : b e t a _ p d f C f u n c t i o n
// -----------------------------------------------------------------------
// Bind pdf ROOT::Math::Beta with three variables as RooAbsPdf function
RooRealVar x2("x2","x2",0,0.999) ;
RooRealVar a("a","a",5,0,10) ;
RooRealVar b("b","b",2,0,10) ;
RooAbsPdf* beta = bindPdf("beta",ROOT::Math::beta_pdf,x2,a,b) ;
// Perf beta definition
beta->Print() ;
// Generate some events and fit
RooDataSet* data = beta->generate(x2,10000) ;
beta->fitTo(*data) ;
// Plot data and pdf on frame
RooPlot* frame2 = x2.frame(Title("ROOT::Math::Beta bound as RooFit pdf")) ;
data->plotOn(frame2) ;
beta->plotOn(frame2) ;
// B i n d R O O T T F 1 a s R o o F i t f u n c t i o n
// ---------------------------------------------------------------
// Create a ROOT TF1 function
TF1 *fa1 = new TF1("fa1","sin(x)/x",0,10);
// Create an observable
RooRealVar x3("x3","x3",0.01,20) ;
// Create binding of TF1 object to above observable
RooAbsReal* rfa1 = bindFunction(fa1,x3) ;
// Print rfa1 definition
rfa1->Print() ;
// Make plot frame in observable, plot TF1 binding function
RooPlot* frame3 = x3.frame(Title("TF1 bound as RooFit function")) ;
rfa1->plotOn(frame3) ;
TCanvas* c = new TCanvas("rf105_funcbinding","rf105_funcbinding",1200,400) ;
c->Divide(3) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
}
void fit2015(
TString FileName ="/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/ppData/OniaTree_262163_262328.root",
int oniamode = 2, // oniamode-> 3: Z, 2: Upsilon and 1: J/Psi
bool isData = true, // isData = false for MC, true for Data
bool isPbPb = false, // isPbPb = false for pp, true for PbPb
bool doFit = false ,
bool inExcStat = true // if inExcStat is true, then the excited states are fitted
) {
InputOpt opt;
SetOptions(&opt, isData, isPbPb, oniamode,inExcStat);
if (isPbPb) {
FileName = "/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/PbPbData/OniaTree_262548_262893.root";
} else {
FileName = "/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/ppData/OniaTree_262163_262328.root";
}
int nbins = 1; //ceil((opt.dMuon->M->Max - opt.dMuon->M->Min)/binw);
if (oniamode==1){
nbins = 140;
} else if (oniamode==2) {
nbins = 70;
} else if (oniamode==3) {
nbins = 40;
}
RooWorkspace myws;
TH1F* hDataOS = new TH1F("hDataOS","hDataOS", nbins, opt.dMuon.M.Min, opt.dMuon.M.Max);
makeWorkspace2015(myws, FileName, opt, hDataOS);
RooRealVar* mass = (RooRealVar*) myws.var("invariantMass");
RooDataSet* dataOS_fit = (RooDataSet*) myws.data("dataOS");
RooDataSet* dataSS_fit = (RooDataSet*) myws.data("dataSS");
RooAbsPdf* pdf = NULL;
if (oniamode==3) { doFit=false; }
if (doFit) {
int sigModel=0, bkgModel=0;
if (isData) {
if (oniamode==1){
sigModel = inExcStat ? 2 : 3;
bkgModel = 1;
} else {
sigModel = inExcStat ? 1 : 3; // gaussian
bkgModel = 2;
}
} else {
if (oniamode==1){
sigModel = inExcStat ? 2 : 3; // gaussian
bkgModel = 2;
} else {
sigModel = inExcStat ? 2 : 3; // gaussian
bkgModel = 3;
}
}
if (opt.oniaMode==1) buildModelJpsi2015(myws, sigModel, bkgModel,inExcStat);
else if (opt.oniaMode==2) buildModelUpsi2015(myws, sigModel, bkgModel,inExcStat);
pdf =(RooAbsPdf*) myws.pdf("pdf");
RooFitResult* fitObject = pdf->fitTo(*dataOS_fit,Save(),Hesse(kTRUE),Extended(kTRUE)); // Fit
}
RooPlot* frame = mass->frame(Bins(nbins),Range(opt.dMuon.M.Min, opt.dMuon.M.Max));
RooPlot* frame2 = NULL;
dataSS_fit->plotOn(frame, Name("dataSS_FIT"), MarkerColor(kRed), LineColor(kRed), MarkerSize(1.2));
dataOS_fit->plotOn(frame, Name("dataOS_FIT"), MarkerColor(kBlue), LineColor(kBlue), MarkerSize(1.2));
if (doFit) {
pdf->plotOn(frame,Name("thePdf"),Normalization(dataOS_fit->sumEntries(),RooAbsReal::NumEvent));
RooHist *hpull = frame -> pullHist(0,0,true);
hpull -> SetName("hpull");
frame2 = mass->frame(Title("Pull Distribution"),Bins(nbins),Range(opt.dMuon.M.Min,opt.dMuon.M.Max));
frame2 -> addPlotable(hpull,"PX");
}
drawPlot(frame,frame2, pdf, opt, doFit,inExcStat);
TString OutputFileName = "";
if (isPbPb) {
FileName = "/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/PbPbData/OniaTree_262548_262893.root";
opt.RunNb.Start=262548;
opt.RunNb.End=262893;
if (oniamode==1) {OutputFileName = (TString)("JPSIPbPbDataset.root");}
if (oniamode==2) {OutputFileName = (TString)("YPbPbDataset.root");}
if (oniamode==3) {OutputFileName = (TString)("ZPbPbDataset.root");}
} else {
FileName = "/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/ppData/OniaTree_262163_262328.root";
opt.RunNb.Start=262163;
opt.RunNb.End=262328;
if (oniamode==1) {OutputFileName = (TString)("JPSIppDataset.root");}
if (oniamode==2) {OutputFileName = (TString)("YppDataset.root");}
if (oniamode==3) {OutputFileName = (TString)("ZppDataset.root");}
}
TFile* oFile = new TFile(OutputFileName,"RECREATE");
oFile->cd();
hDataOS->Write("hDataOS");
dataOS_fit->Write("dataOS_FIT");
oFile->Write();
oFile->Close();
}
void ws_constrained_profile3D( const char* wsfile = "rootfiles/ws-data-unblind.root",
const char* new_poi_name = "n_M234_H4_3b",
int npoiPoints = 20,
double poiMinVal = 0.,
double poiMaxVal = 20.,
double constraintWidth = 1.5,
double ymax = 10.,
int verbLevel=0 ) {
gStyle->SetOptStat(0) ;
//--- make output directory.
char command[10000] ;
sprintf( command, "basename %s", wsfile ) ;
TString wsfilenopath = gSystem->GetFromPipe( command ) ;
wsfilenopath.ReplaceAll(".root","") ;
char outputdirstr[1000] ;
sprintf( outputdirstr, "outputfiles/scans-%s", wsfilenopath.Data() ) ;
TString outputdir( outputdirstr ) ;
printf("\n\n Creating output directory: %s\n\n", outputdir.Data() ) ;
sprintf(command, "mkdir -p %s", outputdir.Data() ) ;
gSystem->Exec( command ) ;
//--- Tell RooFit to shut up about anything less important than an ERROR.
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ;
if ( verbLevel > 0 ) { printf("\n\n Verbose level : %d\n\n", verbLevel) ; }
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
if ( verbLevel > 0 ) { ws->Print() ; }
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
if ( verbLevel > 0 ) {
printf("\n\n\n ===== RooDataSet ====================\n\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
}
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
RooRealVar* rrv_mu_susy_all0lep = ws->var("mu_susy_all0lep") ;
if ( rrv_mu_susy_all0lep == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_all0lep in workspace. Quitting.\n\n\n") ;
return ;
}
//-- do BG only.
rrv_mu_susy_all0lep->setVal(0.) ;
rrv_mu_susy_all0lep->setConstant( kTRUE ) ;
//-- do a prefit.
printf("\n\n\n ====== Pre fit with unmodified nll var.\n\n") ;
RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true),Hesse(false),Minos(false),Strategy(1),PrintLevel(verbLevel));
int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ;
if ( dataSusyFixedFitCovQual < 2 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; }
double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ;
if ( verbLevel > 0 ) {
dataFitResultSusyFixed->Print("v") ;
}
printf("\n\n Nll value, from fit result : %.3f\n\n", dataFitSusyFixedNll ) ;
delete dataFitResultSusyFixed ;
//-- Construct the new POI parameter.
RooAbsReal* new_poi_rar(0x0) ;
new_poi_rar = ws->var( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ;
new_poi_rar = ws->function( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ;
return ;
}
} else {
printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
}
if ( npoiPoints <=0 ) {
printf("\n\n Quitting now.\n\n" ) ;
return ;
}
double startPoiVal = new_poi_rar->getVal() ;
//--- The RooNLLVar is NOT equivalent to what minuit uses.
// RooNLLVar* nll = new RooNLLVar("nll","nll", *likelihood, *rds ) ;
// printf("\n\n Nll value, from construction : %.3f\n\n", nll->getVal() ) ;
//--- output of createNLL IS what minuit uses, so use that.
RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;
RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
/// rrv_poiValue->setVal( poiMinVal ) ;
/// rrv_poiValue->setConstant(kTRUE) ;
RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
rrv_constraintWidth -> setVal( constraintWidth ) ;
rrv_constraintWidth -> setConstant(kTRUE) ;
if ( verbLevel > 0 ) {
printf("\n\n ======= debug likelihood print\n\n") ;
likelihood->Print("v") ;
printf("\n\n ======= debug nll print\n\n") ;
nll->Print("v") ;
}
//----------------------------------------------------------------------------------------------
RooMinuit* rminuit( 0x0 ) ;
RooMinuit* rminuit_uc = new RooMinuit( *nll ) ;
rminuit_uc->setPrintLevel(verbLevel-1) ;
rminuit_uc->setNoWarn() ;
rminuit_uc->migrad() ;
rminuit_uc->hesse() ;
RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;
double floatParInitVal[10000] ;
char floatParName[10000][100] ;
int nFloatParInitVal(0) ;
RooArgList ral_floats = rfr_uc->floatParsFinal() ;
TIterator* floatParIter = ral_floats.createIterator() ;
{
RooRealVar* par ;
while ( (par = (RooRealVar*) floatParIter->Next()) ) {
sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
floatParInitVal[nFloatParInitVal] = par->getVal() ;
nFloatParInitVal++ ;
}
}
//-------
printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
double best_poi_val = new_poi_rar->getVal() ;
char minuit_formula[10000] ;
sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
nll->GetName(),
rrv_constraintWidth->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName()
) ;
printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
RooArgList( *nll,
*rrv_constraintWidth,
*new_poi_rar, *rrv_poiValue,
*new_poi_rar, *rrv_poiValue
) ) ;
printf("\n\n Current value is %.2f\n\n",
new_minuit_var->getVal() ) ;
rminuit = new RooMinuit( *new_minuit_var ) ;
RooAbsReal* plot_var = nll ;
printf("\n\n Current value is %.2f\n\n",
plot_var->getVal() ) ;
rminuit->setPrintLevel(verbLevel-1) ;
if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }
//----------------------------------------------------------------------------------------------
//-- If POI range is -1 to -1, automatically determine the range using the set value.
if ( poiMinVal < 0. && poiMaxVal < 0. ) {
printf("\n\n Automatic determination of scan range.\n\n") ;
if ( startPoiVal <= 0. ) {
printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ;
return ;
}
poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;
if ( poiMinVal < 0. ) { poiMinVal = 0. ; }
printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;
}
//----------------------------------------------------------------------------------------------
double poiVals_scanDown[1000] ;
double nllVals_scanDown[1000] ;
//-- Do scan down from best value.
printf("\n\n +++++ Starting scan down from best value.\n\n") ;
double minNllVal(1.e9) ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
nllVals_scanDown[poivi] = plot_var->getVal() ;
if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }
delete rfr ;
} // poivi
printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;
for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
RooRealVar* par = ws->var( floatParName[pi] ) ;
par->setVal( floatParInitVal[pi] ) ;
} // pi.
printf("\n\n +++++ Starting scan up from best value.\n\n") ;
//-- Now do scan up.
double poiVals_scanUp[1000] ;
double nllVals_scanUp[1000] ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
nllVals_scanUp[poivi] = plot_var->getVal() ;
if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }
delete rfr ;
} // poivi
double poiVals[1000] ;
double nllVals[1000] ;
int pointCount(0) ;
for ( int pi=0; pi<npoiPoints/2; pi++ ) {
poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
pointCount++ ;
}
for ( int pi=1; pi<npoiPoints/2; pi++ ) {
poiVals[pointCount] = poiVals_scanUp[pi] ;
nllVals[pointCount] = nllVals_scanUp[pi] ;
pointCount++ ;
}
npoiPoints = pointCount ;
printf("\n\n --- TGraph arrays:\n") ;
for ( int i=0; i<npoiPoints; i++ ) {
printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
}
printf("\n\n") ;
double nllDiffVals[1000] ;
double poiAtMinlnL(-1.) ;
double poiAtMinusDelta2(-1.) ;
double poiAtPlusDelta2(-1.) ;
for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
} // poivi
printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;
//--- Main canvas
TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
if ( cscan == 0x0 ) {
printf("\n Creating canvas.\n\n") ;
cscan = new TCanvas("cscan","Delta nll") ;
}
char gname[1000] ;
TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
sprintf( gname, "scan_%s", new_poi_name ) ;
graph->SetName( gname ) ;
double poiBest(-1.) ;
double poiMinus1stdv(-1.) ;
double poiPlus1stdv(-1.) ;
double poiMinus2stdv(-1.) ;
double poiPlus2stdv(-1.) ;
double twoDeltalnLMin(1e9) ;
int nscan(1000) ;
for ( int xi=0; xi<nscan; xi++ ) {
double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;
double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;
if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }
}
printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n",
poiBest,
(poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
(poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
) ;
printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n",
new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;
char htitle[1000] ;
sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
hscan->SetMinimum(0.) ;
hscan->SetMaximum(ymax) ;
hscan->DrawCopy() ;
graph->SetLineColor(4) ;
graph->SetLineWidth(3) ;
graph->Draw("CP") ;
gPad->SetGridx(1) ;
gPad->SetGridy(1) ;
cscan->Update() ;
TLine* line = new TLine() ;
line->SetLineColor(2) ;
line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;
TText* text = new TText() ;
text->SetTextSize(0.04) ;
char tstring[1000] ;
sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
text -> DrawTextNDC( 0.15, 0.85, tstring ) ;
sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
text -> DrawTextNDC( 0.15, 0.78, tstring ) ;
char hname[1000] ;
sprintf( hname, "hscanout_%s", new_poi_name ) ;
TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
double obsVal(-1.) ;
hsout->SetBinContent(1, obsVal ) ;
hsout->SetBinContent(2, poiPlus1stdv ) ;
hsout->SetBinContent(3, poiBest ) ;
hsout->SetBinContent(4, poiMinus1stdv ) ;
TAxis* xaxis = hsout->GetXaxis() ;
xaxis->SetBinLabel(1,"Observed val.") ;
xaxis->SetBinLabel(2,"Model+1sd") ;
xaxis->SetBinLabel(3,"Model") ;
xaxis->SetBinLabel(4,"Model-1sd") ;
char outrootfile[10000] ;
sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;
char outpdffile[10000] ;
sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;
cscan->Update() ; cscan->Draw() ;
printf("\n Saving %s\n", outpdffile ) ;
cscan->SaveAs( outpdffile ) ;
//--- save in root file
printf("\n Saving %s\n", outrootfile ) ;
TFile fout(outrootfile,"recreate") ;
graph->Write() ;
hsout->Write() ;
fout.Close() ;
delete ws ;
wstf->Close() ;
}
void fitqual_plots( const char* wsfile = "outputfiles/ws.root", const char* plottitle="" ) {
TText* tt_title = new TText() ;
tt_title -> SetTextAlign(33) ;
gStyle -> SetOptStat(0) ;
gStyle -> SetLabelSize( 0.06, "y" ) ;
gStyle -> SetLabelSize( 0.08, "x" ) ;
gStyle -> SetLabelOffset( 0.010, "y" ) ;
gStyle -> SetLabelOffset( 0.010, "x" ) ;
gStyle -> SetTitleSize( 0.07, "y" ) ;
gStyle -> SetTitleSize( 0.05, "x" ) ;
gStyle -> SetTitleOffset( 1.50, "x" ) ;
gStyle -> SetTitleH( 0.07 ) ;
gStyle -> SetPadLeftMargin( 0.15 ) ;
gStyle -> SetPadBottomMargin( 0.15 ) ;
gStyle -> SetTitleX( 0.10 ) ;
gDirectory->Delete("h*") ;
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
int bins_of_met = TMath::Nint( ws->var("bins_of_met")->getVal() ) ;
printf("\n\n Bins of MET : %d\n\n", bins_of_met ) ;
int bins_of_nb = TMath::Nint( ws->var("bins_of_nb")->getVal() ) ;
printf("\n\n Bins of nb : %d\n\n", bins_of_nb ) ;
int nb_lookup[10] ;
if ( bins_of_nb == 2 ) {
nb_lookup[0] = 2 ;
nb_lookup[1] = 4 ;
} else if ( bins_of_nb == 3 ) {
nb_lookup[0] = 2 ;
nb_lookup[1] = 3 ;
nb_lookup[2] = 4 ;
}
TCanvas* cfq1 = (TCanvas*) gDirectory->FindObject("cfq1") ;
if ( cfq1 == 0x0 ) {
if ( bins_of_nb == 3 ) {
cfq1 = new TCanvas("cfq1","hbb fit", 700, 1000 ) ;
} else if ( bins_of_nb == 2 ) {
cfq1 = new TCanvas("cfq1","hbb fit", 700, 750 ) ;
} else {
return ;
}
}
RooRealVar* rv_sig_strength = ws->var("sig_strength") ;
if ( rv_sig_strength == 0x0 ) { printf("\n\n *** can't find sig_strength in workspace.\n\n" ) ; return ; }
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
RooDataSet* rds = (RooDataSet*) ws->obj( "hbb_observed_rds" ) ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
///RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0) ) ;
RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(3) ) ;
fitResult->Print() ;
char hname[1000] ;
char htitle[1000] ;
char pname[1000] ;
//-- unpack observables.
int obs_N_msig[10][50] ; // first index is n btags, second is met bin.
int obs_N_msb[10][50] ; // first index is n btags, second is met bin.
const RooArgSet* dsras = rds->get() ;
TIterator* obsIter = dsras->createIterator() ;
while ( RooRealVar* obs = (RooRealVar*) obsIter->Next() ) {
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "N_%db_msig_met%d", nb_lookup[nbi], mbi+1 ) ;
if ( strcmp( obs->GetName(), pname ) == 0 ) { obs_N_msig[nbi][mbi] = TMath::Nint( obs -> getVal() ) ; }
sprintf( pname, "N_%db_msb_met%d", nb_lookup[nbi], mbi+1 ) ;
if ( strcmp( obs->GetName(), pname ) == 0 ) { obs_N_msb[nbi][mbi] = TMath::Nint( obs -> getVal() ) ; }
} // mbi.
} // nbi.
} // obs iterator.
printf("\n\n") ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
printf(" nb=%d : ", nb_lookup[nbi] ) ;
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
printf(" sig=%3d, sb=%3d |", obs_N_msig[nbi][mbi], obs_N_msb[nbi][mbi] ) ;
} // mbi.
printf("\n") ;
} // nbi.
printf("\n\n") ;
int pad(1) ;
cfq1->Clear() ;
cfq1->Divide( 2, bins_of_nb+1 ) ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
sprintf( hname, "h_bg_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_bg_msig = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_bg_msig -> SetFillColor( kBlue-9 ) ;
labelBins( hist_bg_msig ) ;
sprintf( hname, "h_bg_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sb, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_bg_msb = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_bg_msb -> SetFillColor( kBlue-9 ) ;
labelBins( hist_bg_msb ) ;
sprintf( hname, "h_sig_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_sig_msig = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_sig_msig -> SetFillColor( kMagenta+2 ) ;
labelBins( hist_sig_msig ) ;
sprintf( hname, "h_sig_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sb, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_sig_msb = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_sig_msb -> SetFillColor( kMagenta+2 ) ;
labelBins( hist_sig_msb ) ;
sprintf( hname, "h_all_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_all_msig = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
sprintf( hname, "h_all_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sb, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_all_msb = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
sprintf( hname, "h_data_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_data_msig = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_data_msig -> SetLineWidth(2) ;
hist_data_msig -> SetMarkerStyle(20) ;
labelBins( hist_data_msig ) ;
sprintf( hname, "h_data_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sb, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_data_msb = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_data_msb -> SetLineWidth(2) ;
hist_data_msb -> SetMarkerStyle(20) ;
labelBins( hist_data_msb ) ;
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "mu_bg_%db_msig_met%d", nb_lookup[nbi], mbi+1 ) ;
RooAbsReal* mu_bg_msig = ws->function( pname ) ;
if ( mu_bg_msig == 0x0 ) { printf("\n\n *** ws missing %s\n\n", pname ) ; return ; }
hist_bg_msig -> SetBinContent( mbi+1, mu_bg_msig->getVal() ) ;
sprintf( pname, "mu_sig_%db_msig_met%d", nb_lookup[nbi], mbi+1 ) ;
RooAbsReal* mu_sig_msig = ws->function( pname ) ;
if ( mu_sig_msig == 0x0 ) { printf("\n\n *** ws missing %s\n\n", pname ) ; return ; }
hist_sig_msig -> SetBinContent( mbi+1, mu_sig_msig->getVal() ) ;
hist_all_msig -> SetBinContent( mbi+1, mu_bg_msig->getVal() + mu_sig_msig->getVal() ) ;
hist_data_msig -> SetBinContent( mbi+1, obs_N_msig[nbi][mbi] ) ;
sprintf( pname, "mu_bg_%db_msb_met%d", nb_lookup[nbi], mbi+1 ) ;
RooAbsReal* mu_bg_msb = ws->function( pname ) ;
if ( mu_bg_msb == 0x0 ) { printf("\n\n *** ws missing %s\n\n", pname ) ; return ; }
hist_bg_msb -> SetBinContent( mbi+1, mu_bg_msb->getVal() ) ;
sprintf( pname, "mu_sig_%db_msb_met%d", nb_lookup[nbi], mbi+1 ) ;
RooAbsReal* mu_sig_msb = ws->function( pname ) ;
if ( mu_sig_msb == 0x0 ) { printf("\n\n *** ws missing %s\n\n", pname ) ; return ; }
hist_sig_msb -> SetBinContent( mbi+1, mu_sig_msb->getVal() ) ;
hist_all_msb -> SetBinContent( mbi+1, mu_bg_msb->getVal() + mu_sig_msb->getVal() ) ;
hist_data_msb -> SetBinContent( mbi+1, obs_N_msb[nbi][mbi] ) ;
} // mbi.
cfq1->cd( pad ) ;
sprintf( hname, "h_stack_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
THStack* hstack_msig = new THStack( hname, htitle ) ;
hstack_msig -> Add( hist_bg_msig ) ;
hstack_msig -> Add( hist_sig_msig ) ;
hist_data_msig -> Draw("e") ;
hstack_msig -> Draw("same") ;
hist_data_msig -> Draw("same e") ;
hist_data_msig -> Draw("same axis") ;
tt_title -> DrawTextNDC( 0.85, 0.85, plottitle ) ;
pad++ ;
cfq1->cd( pad ) ;
sprintf( hname, "h_stack_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
THStack* hstack_msb = new THStack( hname, htitle ) ;
hstack_msb -> Add( hist_bg_msb ) ;
hstack_msb -> Add( hist_sig_msb ) ;
hist_data_msb -> Draw("e") ;
hstack_msb -> Draw("same") ;
hist_data_msb -> Draw("same e") ;
hist_data_msb -> Draw("same axis") ;
tt_title -> DrawTextNDC( 0.85, 0.85, plottitle ) ;
pad++ ;
} // nbi.
TH1F* hist_R_msigmsb = new TH1F( "h_R_msigmsb", "R msig/msb vs met bin", bins_of_met, 0.5, 0.5+bins_of_met ) ;
hist_R_msigmsb -> SetLineWidth(2) ;
hist_R_msigmsb -> SetMarkerStyle(20) ;
hist_R_msigmsb -> SetYTitle("R msig/msb") ;
labelBins( hist_R_msigmsb ) ;
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "R_msigmsb_met%d", mbi+1 ) ;
RooRealVar* rrv_R = ws->var( pname ) ;
if ( rrv_R == 0x0 ) { printf("\n\n *** Can't find %s in ws.\n\n", pname ) ; return ; }
hist_R_msigmsb -> SetBinContent( mbi+1, rrv_R -> getVal() ) ;
hist_R_msigmsb -> SetBinError( mbi+1, rrv_R -> getError() ) ;
} // mbi.
cfq1->cd( pad ) ;
gPad->SetGridy(1) ;
hist_R_msigmsb -> SetMaximum(0.35) ;
hist_R_msigmsb -> Draw("e") ;
tt_title -> DrawTextNDC( 0.85, 0.85, plottitle ) ;
pad++ ;
cfq1->cd( pad ) ;
scan_sigstrength( wsfile ) ;
tt_title -> DrawTextNDC( 0.85, 0.25, plottitle ) ;
TString pdffile( wsfile ) ;
pdffile.ReplaceAll("ws-","fitqual-") ;
pdffile.ReplaceAll("root","pdf") ;
cfq1->SaveAs( pdffile ) ;
TString histfile( wsfile ) ;
histfile.ReplaceAll("ws-","fitqual-") ;
saveHist( histfile, "h*" ) ;
} // fitqual_plots
void paper_fit_plot()
{
SetAtlasStyle();
// get the stuff from the file
char fname_postfit[200] = "monoh_withsm_SRCR_bg11.7_bgslop-0.0_nsig0.0.root";
TFile *tf = new TFile(fname_postfit);
RooWorkspace *ws_post = (RooWorkspace*)tf->Get("wspace");
// data and pdf
RooAbsData *data = ws_post->data("data");
RooAbsPdf *pdfc = ws_post->pdf("jointModeld");
// variables to be constrained
RooRealVar *mh = ws_post->var("mh");
RooRealVar *sigma_h = ws_post->var("sigma_h");
RooRealVar *eff = ws_post->var("eff");
RooRealVar *theory = ws_post->var("theory");
RooRealVar *lumi = ws_post->var("lumi");
RooRealVar *x_mgg = ws_post->var("mgg");
RooArgSet cas(*mh,*sigma_h,*eff,*theory,*lumi);
// redo the fit
RooFitResult *r = pdfc->fitTo(*data,RooFit::Constrain(cas),RooFit::Save(true));
// make the frame
RooPlot *frame = x_mgg->frame();
TCanvas *tc = new TCanvas("tc","",700,500);
frame->GetXaxis()->SetTitle("m_{#gamma#gamma} [GeV]");
frame->GetYaxis()->SetTitle("Events / 1 GeV");
// add the data
data->plotOn(frame,RooFit::Binning(55),RooFit::Name("xdata"),RooFit::DataError(RooAbsData::Poisson));
//data->plotOn(frame,RooFit::Binning(11),RooFit::Name("xdata"),RooFit::DataError(RooAbsData::Poisson));
/// PDFs
pdfc->plotOn(frame, RooFit::Components("E"), RooFit::LineColor(kRed), RooFit::Name("xbackground"));
pdfc->plotOn(frame, RooFit::Components("S"), RooFit::LineColor(kBlue),RooFit::LineStyle(kDotted), RooFit::Name("xsm"));
pdfc->plotOn(frame, RooFit::Components("G"), RooFit::LineColor(kGreen),RooFit::LineStyle(kDotted), RooFit::Name("xbsm"));
pdfc->plotOn(frame, RooFit::LineColor(kRed), RooFit::LineStyle(kDashed),RooFit::Name("xtotal"));
frame->SetMinimum(1e-7);
frame->SetMaximum(9);
// Legend
double x1,y1,x2,y2;
GetX1Y1X2Y2(tc,x1,y1,x2,y2);
TLegend *legend_sr=FastLegend(x1+0.02,y2-0.3,x1+0.35,y2-0.02,0.045);
legend_sr->AddEntry(frame->findObject("xdata"),"Data","LEP");
legend_sr->AddEntry(frame->findObject("xbackground"),"Background fit","L");
legend_sr->AddEntry(frame->findObject("xsm"),"SM H","L");
legend_sr->AddEntry(frame->findObject("xbsm"),"Best-fit BSM H","L");
legend_sr->AddEntry(frame->findObject("xtotal"),"Total","L");
frame->Draw();
legend_sr->Draw("SAME");
// descriptive text
vector<TString> pavetext11;
pavetext11.push_back("#bf{#it{ATLAS Internal}}");
pavetext11.push_back("#sqrt{#it{s}} = 8 TeV #scale[0.6]{#int}#it{L} dt = 20.3 fb^{-1}");
pavetext11.push_back("#it{H + E}_{T}^{miss} , #it{H #rightarrow #gamma#gamma}, #it{m}_{#it{H}} = 125.4 GeV");
TPaveText* text11=CreatePaveText(x2-0.47,y2-0.25,x2-0.05,y2-0.05,pavetext11,0.045);
text11->Draw();
tc->Print("paper_fit_plot.pdf");
}
result fit_toy(RooWorkspace* wspace, int n, const RooArgSet* globals) {
RooRandom::randomGenerator()->SetSeed(0);
// TFile f(filename);
// RooWorkspace *wspace = (RooWorkspace*)f.Get("combined");
ModelConfig* model = (ModelConfig*)wspace->obj("ModelConfig");
RooAbsPdf* pdf;
pdf = model->GetPdf();
RooAbsPdf* top_constraint = (RooAbsPdf*)wspace->obj("top_ratio_constraint");
RooAbsPdf* vv_constraint = (RooAbsPdf*)wspace->obj("vv_ratio_constraint");
RooAbsPdf* top_vv_constraint_sf = (RooAbsPdf*)wspace->obj("top_vv_ratio_sf_constraint");
RooAbsPdf* top_vv_constraint_of = (RooAbsPdf*)wspace->obj("top_vv_ratio_of_constraint");
// generate constraint global observables
RooRealVar *nom_top_ratio = (RooRealVar*)wspace->obj("nom_top_ratio");
nom_top_ratio->setRange(0, 100);
RooRealVar *nom_vv_ratio = (RooRealVar*)wspace->obj("nom_vv_ratio");
nom_vv_ratio->setRange(0,100);
RooRealVar *nom_top_vv_ratio_sf = (RooRealVar*)wspace->obj("nom_top_vv_ratio_sf");
nom_top_vv_ratio_sf->setRange(0,100);
RooRealVar *nom_top_vv_ratio_of = (RooRealVar*)wspace->obj("nom_top_vv_ratio_of");
nom_top_vv_ratio_of->setRange(0,100);
RooDataSet *nom_top_generated = top_constraint->generateSimGlobal(RooArgSet(*nom_top_ratio), 1);
nom_top_ratio->setVal(((RooRealVar*)nom_top_generated->get(0)->find("nom_top_ratio"))->getVal());
RooDataSet *nom_vv_generated = vv_constraint->generateSimGlobal(RooArgSet(*nom_vv_ratio), 1);
nom_vv_ratio->setVal(((RooRealVar*)nom_vv_generated->get(0)->find("nom_vv_ratio"))->getVal());
RooDataSet *nom_top_vv_sf_generated = top_vv_constraint_sf->generateSimGlobal(RooArgSet(*nom_top_vv_ratio_sf), 1);
nom_top_vv_ratio_sf->setVal(((RooRealVar*)nom_top_vv_sf_generated->get(0)->find("nom_top_vv_ratio_sf"))->getVal());
RooDataSet *nom_top_vv_of_generated = top_vv_constraint_of->generateSimGlobal(RooArgSet(*nom_top_vv_ratio_of), 1);
nom_top_vv_ratio_of->setVal(((RooRealVar*)nom_top_vv_of_generated->get(0)->find("nom_top_vv_ratio_of"))->getVal());
NumEventsTestStat* dummy = new NumEventsTestStat(*pdf);
ToyMCSampler* mc = new ToyMCSampler(*dummy, 1);
mc->SetPdf(*pdf);
mc->SetObservables(*model->GetObservables());
mc->SetGlobalObservables(*globals);
mc->SetNuisanceParameters(*model->GetNuisanceParameters());
mc->SetParametersForTestStat(*model->GetParametersOfInterest());
mc->SetNEventsPerToy(n);
RooArgSet constr;
constr.add(*(model->GetNuisanceParameters()));
RemoveConstantParameters(&constr);
RooDataSet* toy_data = (RooDataSet*)mc->GenerateToyData(*const_cast<RooArgSet*>(model->GetSnapshot()));
RooFitResult *res = pdf->fitTo(*toy_data, Constrain(constr), PrintLevel(0), Save(),
Range("fitRange"), InitialHesse(),
ExternalConstraints(RooArgSet(*top_constraint, *vv_constraint, *top_vv_constraint_sf, *top_vv_constraint_of)));
result yield = get_results(wspace, res);
yield.of.generated_sum.val = toy_data->sumEntries("(channelCat==channelCat::of) & (obs_x_of>120)");
yield.sf.generated_sum.val = toy_data->sumEntries("(channelCat==channelCat::sf) & (obs_x_sf>120)");
delete mc;
delete dummy;
// f.Close();
return yield;
}
void constrained_scan( const char* wsfile = "outputfiles/ws.root",
const char* new_poi_name="mu_bg_4b_msig_met1",
double constraintWidth=1.5,
int npoiPoints = 20,
double poiMinVal = 0.,
double poiMaxVal = 10.0,
double ymax = 9.,
int verbLevel=1 ) {
TString outputdir("outputfiles") ;
gStyle->SetOptStat(0) ;
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "hbb_observed_rds" ) ;
cout << "\n\n\n ===== RooDataSet ====================\n\n" << endl ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
RooRealVar* rv_sig_strength = ws->var("sig_strength") ;
if ( rv_sig_strength == 0x0 ) { printf("\n\n *** can't find sig_strength in workspace.\n\n" ) ; return ; }
RooAbsPdf* likelihood = ws->pdf("likelihood") ;
if ( likelihood == 0x0 ) { printf("\n\n *** can't find likelihood in workspace.\n\n" ) ; return ; }
printf("\n\n Likelihood:\n") ;
likelihood -> Print() ;
/////rv_sig_strength -> setConstant( kFALSE ) ;
rv_sig_strength -> setVal(0.) ;
rv_sig_strength -> setConstant( kTRUE ) ;
likelihood->fitTo( *rds, Save(false), PrintLevel(0), Hesse(true), Strategy(1) ) ;
//RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0), Hesse(true), Strategy(1) ) ;
//double minNllSusyFloat = fitResult->minNll() ;
//double susy_ss_atMinNll = rv_sig_strength -> getVal() ;
RooMsgService::instance().getStream(1).removeTopic(Minimization) ;
RooMsgService::instance().getStream(1).removeTopic(Fitting) ;
//-- Construct the new POI parameter.
RooAbsReal* new_poi_rar(0x0) ;
new_poi_rar = ws->var( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ;
new_poi_rar = ws->function( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ;
return ;
} else {
printf("\n Found it.\n\n") ;
}
} else {
printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
}
double startPoiVal = new_poi_rar->getVal() ;
RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;
RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
rrv_constraintWidth -> setVal( constraintWidth ) ;
rrv_constraintWidth -> setConstant(kTRUE) ;
RooMinuit* rminuit( 0x0 ) ;
RooMinuit* rminuit_uc = new RooMinuit( *nll ) ;
rminuit_uc->setPrintLevel(verbLevel-1) ;
rminuit_uc->setNoWarn() ;
rminuit_uc->migrad() ;
rminuit_uc->hesse() ;
RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;
double floatParInitVal[10000] ;
char floatParName[10000][100] ;
int nFloatParInitVal(0) ;
RooArgList ral_floats = rfr_uc->floatParsFinal() ;
TIterator* floatParIter = ral_floats.createIterator() ;
{
RooRealVar* par ;
while ( (par = (RooRealVar*) floatParIter->Next()) ) {
sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
floatParInitVal[nFloatParInitVal] = par->getVal() ;
nFloatParInitVal++ ;
}
}
printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
double best_poi_val = new_poi_rar->getVal() ;
char minuit_formula[10000] ;
sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
nll->GetName(),
rrv_constraintWidth->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName()
) ;
printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
RooArgList( *nll,
*rrv_constraintWidth,
*new_poi_rar, *rrv_poiValue,
*new_poi_rar, *rrv_poiValue
) ) ;
printf("\n\n Current value is %.2f\n\n",
new_minuit_var->getVal() ) ;
rminuit = new RooMinuit( *new_minuit_var ) ;
RooAbsReal* plot_var = nll ;
printf("\n\n Current value is %.2f\n\n",
plot_var->getVal() ) ;
rminuit->setPrintLevel(verbLevel-1) ;
if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }
if ( poiMinVal < 0. && poiMaxVal < 0. ) {
printf("\n\n Automatic determination of scan range.\n\n") ;
if ( startPoiVal <= 0. ) {
printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ;
return ;
}
poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;
if ( poiMinVal < 0. ) { poiMinVal = 0. ; }
printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;
}
//----------------------------------------------------------------------------------------------
double poiVals_scanDown[1000] ;
double nllVals_scanDown[1000] ;
//-- Do scan down from best value.
printf("\n\n +++++ Starting scan down from best value.\n\n") ;
double minNllVal(1.e9) ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
nllVals_scanDown[poivi] = plot_var->getVal() ;
if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }
delete rfr ;
} // poivi
printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;
for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
RooRealVar* par = ws->var( floatParName[pi] ) ;
par->setVal( floatParInitVal[pi] ) ;
} // pi.
printf("\n\n +++++ Starting scan up from best value.\n\n") ;
//-- Now do scan up.
double poiVals_scanUp[1000] ;
double nllVals_scanUp[1000] ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
nllVals_scanUp[poivi] = plot_var->getVal() ;
if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }
delete rfr ;
} // poivi
double poiVals[1000] ;
double nllVals[1000] ;
int pointCount(0) ;
for ( int pi=0; pi<npoiPoints/2; pi++ ) {
poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
pointCount++ ;
}
for ( int pi=1; pi<npoiPoints/2; pi++ ) {
poiVals[pointCount] = poiVals_scanUp[pi] ;
nllVals[pointCount] = nllVals_scanUp[pi] ;
pointCount++ ;
}
npoiPoints = pointCount ;
printf("\n\n --- TGraph arrays:\n") ;
for ( int i=0; i<npoiPoints; i++ ) {
printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
}
printf("\n\n") ;
double nllDiffVals[1000] ;
double poiAtMinlnL(-1.) ;
double poiAtMinusDelta2(-1.) ;
double poiAtPlusDelta2(-1.) ;
for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
} // poivi
printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;
//--- Main canvas
TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
if ( cscan == 0x0 ) {
printf("\n Creating canvas.\n\n") ;
cscan = new TCanvas("cscan","Delta nll") ;
}
char gname[1000] ;
TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
sprintf( gname, "scan_%s", new_poi_name ) ;
graph->SetName( gname ) ;
double poiBest(-1.) ;
double poiMinus1stdv(-1.) ;
double poiPlus1stdv(-1.) ;
double poiMinus2stdv(-1.) ;
double poiPlus2stdv(-1.) ;
double twoDeltalnLMin(1e9) ;
int nscan(1000) ;
for ( int xi=0; xi<nscan; xi++ ) {
double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;
double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;
if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }
}
printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n",
poiBest,
(poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
(poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
) ;
printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n",
new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;
char htitle[1000] ;
sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
hscan->SetMinimum(0.) ;
hscan->SetMaximum(ymax) ;
hscan->DrawCopy() ;
graph->SetLineColor(4) ;
graph->SetLineWidth(3) ;
graph->Draw("CP") ;
gPad->SetGridx(1) ;
gPad->SetGridy(1) ;
cscan->Update() ;
TLine* line = new TLine() ;
line->SetLineColor(2) ;
line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;
TText* text = new TText() ;
text->SetTextSize(0.04) ;
char tstring[1000] ;
sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
text -> DrawTextNDC( 0.15, 0.85, tstring ) ;
sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
text -> DrawTextNDC( 0.15, 0.78, tstring ) ;
char hname[1000] ;
sprintf( hname, "hscanout_%s", new_poi_name ) ;
TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
double obsVal(-1.) ;
hsout->SetBinContent(1, obsVal ) ;
hsout->SetBinContent(2, poiPlus1stdv ) ;
hsout->SetBinContent(3, poiBest ) ;
hsout->SetBinContent(4, poiMinus1stdv ) ;
TAxis* xaxis = hsout->GetXaxis() ;
xaxis->SetBinLabel(1,"Observed val.") ;
xaxis->SetBinLabel(2,"Model+1sd") ;
xaxis->SetBinLabel(3,"Model") ;
xaxis->SetBinLabel(4,"Model-1sd") ;
char outrootfile[10000] ;
sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;
char outpdffile[10000] ;
sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;
cscan->Update() ; cscan->Draw() ;
printf("\n Saving %s\n", outpdffile ) ;
cscan->SaveAs( outpdffile ) ;
//--- save in root file
printf("\n Saving %s\n", outrootfile ) ;
TFile fout(outrootfile,"recreate") ;
graph->Write() ;
hsout->Write() ;
fout.Close() ;
delete ws ;
wstf->Close() ;
} // constrained_scan.
//____________________________________
void rs_bernsteinCorrection(){
// set range of observable
Double_t lowRange = -1, highRange =5;
// make a RooRealVar for the observable
RooRealVar x("x", "x", lowRange, highRange);
// true model
RooGaussian narrow("narrow","",x,RooConst(0.), RooConst(.8));
RooGaussian wide("wide","",x,RooConst(0.), RooConst(2.));
RooAddPdf reality("reality","",RooArgList(narrow, wide), RooConst(0.8));
RooDataSet* data = reality.generate(x,1000);
// nominal model
RooRealVar sigma("sigma","",1.,0,10);
RooGaussian nominal("nominal","",x,RooConst(0.), sigma);
RooWorkspace* wks = new RooWorkspace("myWorksspace");
wks->import(*data, Rename("data"));
wks->import(nominal);
// The tolerance sets the probability to add an unecessary term.
// lower tolerance will add fewer terms, while higher tolerance
// will add more terms and provide a more flexible function.
Double_t tolerance = 0.05;
BernsteinCorrection bernsteinCorrection(tolerance);
Int_t degree = bernsteinCorrection.ImportCorrectedPdf(wks,"nominal","x","data");
cout << " Correction based on Bernstein Poly of degree " << degree << endl;
RooPlot* frame = x.frame();
data->plotOn(frame);
// plot the best fit nominal model in blue
nominal.fitTo(*data,PrintLevel(-1));
nominal.plotOn(frame);
// plot the best fit corrected model in red
RooAbsPdf* corrected = wks->pdf("corrected");
corrected->fitTo(*data,PrintLevel(-1));
corrected->plotOn(frame,LineColor(kRed));
// plot the correction term (* norm constant) in dashed green
// should make norm constant just be 1, not depend on binning of data
RooAbsPdf* poly = wks->pdf("poly");
poly->plotOn(frame,LineColor(kGreen), LineStyle(kDashed));
// this is a switch to check the sampling distribution
// of -2 log LR for two comparisons:
// the first is for n-1 vs. n degree polynomial corrections
// the second is for n vs. n+1 degree polynomial corrections
// Here we choose n to be the one chosen by the tolerance
// critereon above, eg. n = "degree" in the code.
// Setting this to true is takes about 10 min.
bool checkSamplingDist = false;
TCanvas* c1 = new TCanvas();
if(checkSamplingDist) {
c1->Divide(1,2);
c1->cd(1);
}
frame->Draw();
if(checkSamplingDist) {
// check sampling dist
TH1F* samplingDist = new TH1F("samplingDist","",20,0,10);
TH1F* samplingDistExtra = new TH1F("samplingDistExtra","",20,0,10);
int numToyMC = 1000;
bernsteinCorrection.CreateQSamplingDist(wks,"nominal","x","data",samplingDist, samplingDistExtra, degree,numToyMC);
c1->cd(2);
samplingDistExtra->SetLineColor(kRed);
samplingDistExtra->Draw();
samplingDist->Draw("same");
}
}
void MakePlots(RooWorkspace* ws){
std::cout << "make plots" << std::endl;
RooAbsPdf* model = ws->pdf("model");
RooAbsPdf* model_dY = ws->pdf("model_dY");
RooRealVar* nsig = ws->var("nsig");
RooRealVar* nsigDPS = ws->var("nsigDPS");
RooRealVar* nsigSPS = ws->var("nsigSPS");
RooRealVar* nBbkg = ws->var("nBbkg");
RooRealVar* nbkg = ws->var("nbkg");
RooRealVar* nbkg2 = ws->var("nbkg2");
RooRealVar* FourMu_Mass = ws->var("FourMu_Mass");
RooRealVar* Psi1_Mass = ws->var("Psi1_Mass");
RooRealVar* Psi2_Mass = ws->var("Psi2_Mass");
RooRealVar* Psi1_CTxy = ws->var("Psi1_CTxy");
RooRealVar* Psi2_CTxy = ws->var("Psi2_CTxy");
RooRealVar* Psi1To2_dY = ws->var("Psi1To2_dY");
RooRealVar* Psi1To2Significance = ws->var("Psi1To2Significance");
// note, we get the dataset with sWeights
RooDataSet* data = (RooDataSet*) ws->data("dataWithSWeights");
model->fitTo(*data, Extended() );
// make TTree with efficiency variation info
//TFile *fFile = new TFile("Fit_Results_Eff_Cut.root","recreate");
setTDRStyle();
// make our canvas
TCanvas* cmass1 = new TCanvas("sPlotMass1","sPlotMass1", 600, 600);
cmass1->cd();
cmass1->SetFillColor(kWhite);
RooPlot* Mass1Plot = Psi1_Mass->frame(20);
data->plotOn(Mass1Plot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(Mass1Plot,Name("all"));
model->plotOn(Mass1Plot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(Mass1Plot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(Mass1Plot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(Mass1Plot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
Mass1Plot->SetTitle("");
Mass1Plot->SetXTitle("#mu^{+}#mu^{-} 1 Invariant Mass (GeV/c^{2})");
Mass1Plot->SetYTitle("Events / 0.025 GeV/c^{2}");
Mass1Plot->SetLabelOffset(0.012);
//Mass1Plot->SetTitleOffset(0.95);
Mass1Plot->Draw();
//cmass1->SaveAs("pic/Psi1_mass.pdf");
//cmass1->Close();
TCanvas* cmass2 = new TCanvas("sPlotMass2","sPlotMass2", 600, 600);
cmass2->cd();
cmass2->SetFillColor(kWhite);
RooPlot* Mass2Plot = Psi2_Mass->frame(20);
data->plotOn(Mass2Plot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(Mass2Plot,Name("all"));
model->plotOn(Mass2Plot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(Mass2Plot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(Mass2Plot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(Mass2Plot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
Mass2Plot->SetTitle("");
Mass2Plot->SetXTitle("#mu^{+}#mu^{-} 2 Invariant Mass (GeV/c^{2})");
Mass2Plot->SetYTitle("Events / 0.025 GeV/c^{2}");
Mass2Plot->SetLabelOffset(0.012);
//Mass2Plot->SetTitleOffset(0.95);
Mass2Plot->Draw();
//cmass2->SaveAs("pic/Psi2_mass.pdf");
//cmass2->Close();
TCanvas* cctxy1 = new TCanvas("sPlotCTxy1","sPlotCTxy1", 600, 600);
cctxy1->cd();
cctxy1->SetFillColor(kWhite);
RooPlot* CTxy1Plot = Psi1_CTxy->frame(30);
data->plotOn(CTxy1Plot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(CTxy1Plot,Name("all"));
model->plotOn(CTxy1Plot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(CTxy1Plot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(CTxy1Plot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(CTxy1Plot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
CTxy1Plot->SetTitle("");
CTxy1Plot->SetXTitle("J/#psi^{1} ct_{xy} (cm)");
CTxy1Plot->SetYTitle("Events / 0.005 cm");
CTxy1Plot->SetMaximum(2000);
CTxy1Plot->SetMinimum(0.1);
CTxy1Plot->Draw();
cctxy1->SetLogy();
//cctxy1->SaveAs("pic/Psi1_CTxy.pdf");
//cctxy1->Close();
TCanvas* csig = new TCanvas("sPlotSig","sPlotSig", 600, 600);
csig->cd();
RooPlot* SigPlot = Psi1To2Significance->frame(20);
data->plotOn(SigPlot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(SigPlot,Name("all"));
model->plotOn(SigPlot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(SigPlot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(SigPlot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(SigPlot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
SigPlot->SetTitle("");
SigPlot->SetYTitle("Events / 0.4");
SigPlot->SetXTitle("J/#psi Distance Significance");
SigPlot->Draw();
//csig->SaveAs("pic/Psi1To2Significance.pdf");
//csig->Close();
// create weighted data set (signal-weighted)
//RooDataSet * dataw_sig = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"nsig_sw");
// model_dY->fitTo(*data);
//TCanvas* cdata2 = new TCanvas("sPlot2","sPlots2", 700, 500);
//cdata2->cd();
//RooPlot* frame2 = Psi1To2_dY->frame(20);
//dataw_sig->plotOn(frame2, DataError(RooAbsData::SumW2) );
/*
model_dY->fitTo(*dataw_sig, SumW2Error(kTRUE), Extended());
model_dY->plotOn(frame2);
model_dY->plotOn(frame2,RooFit::Components("*DPS"),RooFit::LineColor(kRed), RooFit::LineStyle(kDashed));
model_dY->plotOn(frame2,RooFit::Components("*SPS"),RooFit::LineColor(kGreen), RooFit::LineStyle(kDashed));
*/
//frame2->SetTitle("DeltaY distribution for sig");
//frame2->SetMinimum(1e-03);
//frame2->Draw();
//TCanvas* cdata3 = new TCanvas("sPlot3","sPlots3", 700, 500);
//cdata3->cd();
//RooPlot* frame3 = FourMu_Mass->frame(Bins(14),Range(6.,20.));
//dataw_sig->plotOn(frame3, DataError(RooAbsData::SumW2));
//frame3->SetTitle("FourMu_Mass distribution for sig");
//frame3->SetMinimum(1e-03);
//frame3->Draw();
//fFile->Write();
//fFile->Close();
//delete fFile;
}
void plot_pll(TString fname="monoh_withsm_SRCR_bg11.7_bgslop-0.0_nsig0.0.root")
{
SetAtlasStyle();
TFile* file = TFile::Open(fname);
RooWorkspace* wspace = (RooWorkspace*) file->Get("wspace");
cout << "\n\ncheck that eff and reco terms included in BSM component to make fiducial cross-section" <<endl;
wspace->function("nsig")->Print();
RooRealVar* reco = wspace->var("reco");
if( wspace->function("nsig")->dependsOn(*reco) ) {
cout << "all good." <<endl;
} else {
cout << "need to rerun fit_withsm using DO_FIDUCIAL_LIMIT true" <<endl;
return;
}
/*
// DANGER
// TEST WITH EXAGGERATED UNCERTAINTY
wspace->var("unc_theory")->setMax(1);
wspace->var("unc_theory")->setVal(1);
wspace->var("unc_theory")->Print();
*/
// this was for making plot about decoupling/recoupling approach
TCanvas* tc = new TCanvas("tc","",400,400);
RooPlot *frame = wspace->var("xsec_bsm")->frame();
RooAbsPdf* pdfc = wspace->pdf("jointModeld");
RooAbsData* data = wspace->data("data");
RooAbsReal *nllJoint = pdfc->createNLL(*data, RooFit::Constrained()); // slice with fixed xsec_bsm
RooAbsReal *profileJoint = nllJoint->createProfile(*wspace->var("xsec_bsm"));
wspace->allVars().Print("v");
pdfc->fitTo(*data);
wspace->allVars().Print("v");
wspace->var("xsec_bsm")->Print();
double nllmin = 2*nllJoint->getVal();
wspace->var("xsec_bsm")->setVal(0);
double nll0 = 2*nllJoint->getVal();
cout << Form("nllmin = %f, nll0 = %f, Z=%f", nllmin, nll0, sqrt(nll0-nllmin)) << endl;
nllJoint->plotOn(frame, RooFit::LineColor(kGreen), RooFit::LineStyle(kDotted), RooFit::ShiftToZero(), RooFit::Name("nll_statonly")); // no error
profileJoint->plotOn(frame,RooFit::Name("pll") );
wspace->var("xsec_sm")->Print();
wspace->var("theory")->Print();
wspace->var("theory")->setConstant();
profileJoint->plotOn(frame, RooFit::LineColor(kRed), RooFit::LineStyle(kDashed), RooFit::Name("pll_smfixed") );
frame->GetXaxis()->SetTitle("#sigma_{BSM, fid} [fb]");
frame->GetYaxis()->SetTitle("-log #lambda ( #sigma_{BSM, fid} )");
double temp = frame->GetYaxis()->GetTitleOffset();
frame->GetYaxis()->SetTitleOffset( 1.1* temp );
frame->SetMinimum(1e-7);
frame->SetMaximum(4);
// Legend
double x1,y1,x2,y2;
GetX1Y1X2Y2(tc,x1,y1,x2,y2);
TLegend *legend_sr=FastLegend(x2-0.75,y2-0.3,x2-0.25,y2-0.5,0.045);
legend_sr->AddEntry(frame->findObject("pll"),"with #sigma_{SM} uncertainty","L");
legend_sr->AddEntry(frame->findObject("pll_smfixed"),"with #sigma_{SM} constant","L");
legend_sr->AddEntry(frame->findObject("nll_statonly"),"no systematics","L");
frame->Draw();
legend_sr->Draw("SAME");
// descriptive text
vector<TString> pavetext11;
pavetext11.push_back("#bf{#it{ATLAS Internal}}");
pavetext11.push_back("#sqrt{#it{s}} = 8 TeV #scale[0.6]{#int}Ldt = 20.3 fb^{-1}");
pavetext11.push_back("#it{H}+#it{E}_{T}^{miss} , #it{H #rightarrow #gamma#gamma}, #it{m}_{#it{H}} = 125.4 GeV");
TPaveText* text11=CreatePaveText(x2-0.75,y2-0.25,x2-0.25,y2-0.05,pavetext11,0.045);
text11->Draw();
tc->SaveAs("pll.pdf");
/*
wspace->var("xsec_bsm")->setConstant(true);
wspace->var("eff" )->setConstant(true);
wspace->var("mh" )->setConstant(true);
wspace->var("sigma_h" )->setConstant(true);
wspace->var("lumi" )->setConstant(true);
wspace->var("xsec_sm" )->setVal(v_xsec_sm);
wspace->var("eff" )->setVal(1.0);
wspace->var("lumi" )->setVal(v_lumi);
TH1* nllHist = profileJoint->createHistogram("xsec_bsm",100);
TFile* out = new TFile("nllHist.root","REPLACE");
nllHist->Write()
out->Write();
out->Close();
*/
}
/***********************************************************************
***********************************************************************
* CONSTRUCTOR MAKES ALLLLLLLL
*******************************************************************
*************************************************
*****************************
*****
*/
Tbroomfit(double xlow, double xhi, TH1 *h2, int npeak, double *peak, double *sigm, const char *chpol="p0"){
int iq=0;
printf("constructor - %d %ld", iq++, (int64_t)h2 );
h2->Print();
/*
* get global area, ranges for sigma, x
*/
npeaks=npeak; // class defined int
// double areah2=h2->Integral( int(xlow), int(xhi) ); // WRONG - BINS
min= h2->GetXaxis()->GetFirst();
printf("constructor - %d %f", iq++, min );
max= h2->GetXaxis()->GetLast();
printf("constructor - %d %f", iq++, max );
double areah2=h2->Integral( min, max );
printf("constructor - %d %f", iq++, areah2 );
min=xlow;
max=xhi;
double sigmamin=(max-min)/300;
double sigmamax=(max-min)/4;
double areamin=0;
double areamax=2*areah2;
printf("x:(%f,%f) s:(%f,%f) a:(%f,%f) \n", min,max,sigmamin, sigmamax,areamin, areamax );
/*
* definition of variables..............
*
*/
RooRealVar x("x", "x", min, max);
int MAXPEAKS=6; // later from 5 to 6 ???
printf("RooFit: npeaks=%d\n", npeaks );
// ABOVE: RooRealVar *msat[14][5]; // POINTERS TO ALL variables
// 0 m Mean
// 1 s Sigma
// 2 a Area
// 3 t Tail
// 4 [0] nalpha
// 5 [0] n1
for (int ii=0;ii<14;ii++){
for (int jj=0;jj<MAXPEAKS;jj++){
msat[ii][jj]=NULL;
msat_values[ii][jj]=0.0;
} //for for
}// for for
printf("delete fitresult, why crash?\n%s","");
fitresult=NULL;
printf("delete fitresult, no crash?\n%s","");
RooRealVar mean1("mean1", "mean", 1*(max-min)/(npeaks+1)+min, min,max);msat[0][0]=&mean1;
RooRealVar mean2("mean2", "mean", 2*(max-min)/(npeaks+1)+min, min,max);msat[0][1]=&mean2;
RooRealVar mean3("mean3", "mean", 3*(max-min)/(npeaks+1)+min, min,max);msat[0][2]=&mean3;
RooRealVar mean4("mean4", "mean", 4*(max-min)/(npeaks+1)+min, min,max);msat[0][3]=&mean4;
RooRealVar mean5("mean5", "mean", 5*(max-min)/(npeaks+1)+min, min,max);msat[0][4]=&mean5;
RooRealVar mean6("mean6", "mean", 6*(max-min)/(npeaks+1)+min, min,max);msat[0][5]=&mean6;
RooRealVar sigma1("sigma1","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][0]=&sigma1;
RooRealVar sigma2("sigma2","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][1]=&sigma2;
RooRealVar sigma3("sigma3","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][2]=&sigma3;
RooRealVar sigma4("sigma4","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][3]=&sigma4;
RooRealVar sigma5("sigma5","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][4]=&sigma5;
RooRealVar sigma6("sigma6","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][5]=&sigma6;
RooRealVar area1("area1", "area", areah2/npeaks, areamin, areamax );msat[2][0]=&area1;
RooRealVar area2("area2", "area", areah2/npeaks, areamin, areamax );msat[2][1]=&area2;
RooRealVar area3("area3", "area", areah2/npeaks, areamin, areamax );msat[2][2]=&area3;
RooRealVar area4("area4", "area", areah2/npeaks, areamin, areamax );msat[2][3]=&area4;
RooRealVar area5("area5", "area", areah2/npeaks, areamin, areamax );msat[2][4]=&area5;
RooRealVar area6("area6", "area", areah2/npeaks, areamin, areamax );msat[2][5]=&area6;
RooRealVar bgarea("bgarea", "bgarea", areah2/5, 0, 2*areah2);
double tailstart=-1.0;// tune the tails....
double tailmin=-1e+4;
double tailmax=1e+4;
RooRealVar tail1("tail1", "tail", tailstart, tailmin, tailmax );msat[3][0]=&tail1;
RooRealVar tail2("tail2", "tail", tailstart, tailmin, tailmax );msat[3][1]=&tail2;
RooRealVar tail3("tail3", "tail", tailstart, tailmin, tailmax );msat[3][2]=&tail3;
RooRealVar tail4("tail4", "tail", tailstart, tailmin, tailmax );msat[3][3]=&tail4;
RooRealVar tail5("tail5", "tail", tailstart, tailmin, tailmax );msat[3][4]=&tail5;
RooRealVar tail6("tail6", "tail", tailstart, tailmin, tailmax );msat[3][5]=&tail6;
// for CBShape
RooRealVar nalpha1("nalpha1", "nalpha", 1.3, 0, 100 );msat[4][0]=&nalpha1;
RooRealVar n1("n1", "n", 5.1, 0, 100 ); msat[5][0]=&n1;
/*
* initial values for peak positions................
*/
if (npeaks>=1) {mean1=peak[0];sigma1=sigm[0];}
if (npeaks>=2) {mean2=peak[1];sigma2=sigm[1];}
if (npeaks>=3) {mean3=peak[2];sigma3=sigm[2];}
if (npeaks>=4) {mean4=peak[3];sigma4=sigm[3];}
if (npeaks>=5) {mean5=peak[4];sigma5=sigm[4];}
if (npeaks>=6) {mean6=peak[5];sigma6=sigm[5];}
/*
* RooAbsPdf -> RooGaussian
* RooNovosibirsk
* RooLandau
*/
RooAbsPdf *pk[6]; // MAXIMUM PEAKS ==5 6 NOW!!
RooAbsPdf *pk_dicto[14][6]; // ALL DICTIONARY OF PEAKS..........
// Abstract Class.... carrefuly
RooGaussian gauss1("gauss1","gauss(x,mean,sigma)", x, mean1, sigma1);pk_dicto[0][0]=&gauss1;
RooGaussian gauss2("gauss2","gauss(x,mean,sigma)", x, mean2, sigma2);pk_dicto[0][1]=&gauss2;
RooGaussian gauss3("gauss3","gauss(x,mean,sigma)", x, mean3, sigma3);pk_dicto[0][2]=&gauss3;
RooGaussian gauss4("gauss4","gauss(x,mean,sigma)", x, mean4, sigma4);pk_dicto[0][3]=&gauss4;
RooGaussian gauss5("gauss5","gauss(x,mean,sigma)", x, mean5, sigma5);pk_dicto[0][4]=&gauss5;
RooGaussian gauss6("gauss6","gauss(x,mean,sigma)", x, mean6, sigma6);pk_dicto[0][5]=&gauss6;
RooNovosibirsk ns1("ns1","novosib(x,mean,sigma,tail)", x, mean1,sigma1, tail1 );pk_dicto[1][0]=&ns1;
RooNovosibirsk ns2("ns2","novosib(x,mean,sigma,tail)", x, mean2,sigma2, tail2 );pk_dicto[1][1]=&ns2;
RooNovosibirsk ns3("ns3","novosib(x,mean,sigma,tail)", x, mean3,sigma3, tail3 );pk_dicto[1][2]=&ns3;
RooNovosibirsk ns4("ns4","novosib(x,mean,sigma,tail)", x, mean4,sigma4, tail4 );pk_dicto[1][3]=&ns4;
RooNovosibirsk ns5("ns5","novosib(x,mean,sigma,tail)", x, mean5,sigma5, tail5 );pk_dicto[1][4]=&ns5;
// BreitWiegner is Lorentzian...?
RooBreitWigner bw1("bw1","BreitWigner(x,mean,sigma)", x, mean1, sigma1 );pk_dicto[2][0]=&bw1;
RooBreitWigner bw2("bw2","BreitWigner(x,mean,sigma)", x, mean2, sigma2 );pk_dicto[2][1]=&bw2;
RooBreitWigner bw3("bw3","BreitWigner(x,mean,sigma)", x, mean3, sigma3 );pk_dicto[2][2]=&bw3;
RooBreitWigner bw4("bw4","BreitWigner(x,mean,sigma)", x, mean4, sigma4 );pk_dicto[2][3]=&bw4;
RooBreitWigner bw5("bw5","BreitWigner(x,mean,sigma)", x, mean5, sigma5 );pk_dicto[2][4]=&bw5;
RooCBShape cb1("cb1","CBShape(x,mean,sigma)", x, mean1, sigma1, nalpha1, n1 );pk_dicto[3][0]=&cb1;
RooCBShape cb2("cb2","CBShape(x,mean,sigma)", x, mean2, sigma2, nalpha1, n1 );pk_dicto[3][1]=&cb2;
RooCBShape cb3("cb3","CBShape(x,mean,sigma)", x, mean3, sigma3, nalpha1, n1 );pk_dicto[3][2]=&cb3;
RooCBShape cb4("cb4","CBShape(x,mean,sigma)", x, mean4, sigma4, nalpha1, n1 );pk_dicto[3][3]=&cb4;
RooCBShape cb5("cb5","CBShape(x,mean,sigma)", x, mean5, sigma5, nalpha1, n1 );pk_dicto[3][4]=&cb5;
RooCBShape cb6("cb6","CBShape(x,mean,sigma)", x, mean6, sigma6, nalpha1, n1 );pk_dicto[3][5]=&cb6;
/*
* PEAK TYPES BACKGROUND TYPE ......... COMMAND BOX OPTIONS ......
*/
/****************************************************************************
* PLAY WITH THE DEFINITION COMMANDLINE...................... POLYNOM + PEAKS
*/
// CALSS DECLARED TString s;
s=chpol;
/*
* peaks+bg== ALL BEFORE ; or : (after ... it is a conditions/options)
*/
TString command;
int comstart=s.Index(":"); if (comstart<0){ comstart=s.Index(";");}
if (comstart<0){ command="";}else{
command=s(comstart+1, s.Length()-comstart -1 ); // without ;
s=s(0,comstart); // without ;
printf("COMMANDLINE : %s\n", command.Data() );
if (TPRegexp("scom").Match(command)!=0){
}// COMMANDS -
}// there is some command
/*************************************************
* PLAY WITH peaks+bg.................. s
*/
s.Append("+"); s.Prepend("+"); s.ReplaceAll(" ","+");
s.ReplaceAll("++++","+"); s.ReplaceAll("+++","+"); s.ReplaceAll("++","+");s.ReplaceAll("++","+");
printf (" regextp = %s\n", s.Data() );
if (TPRegexp("\\+p[\\dn]\\+").Match(s)==0){ // no match
printf("NO polynomial demanded =>: %s\n", "appending pn command" ); s.Append("pn+");
}
TString spk=s; TString sbg=s;
TPRegexp("\\+p[\\dn]\\+").Substitute(spk,"+"); // remove +p.+
TPRegexp(".+(p[\\dn]).+").Substitute(sbg,"$1"); // remove all but +p+
printf ("PEAKS=%s BG=%s\n", spk.Data() , sbg.Data() );
spk.ReplaceAll("+",""); // VARIANT 1 ------- EACH LETTER MEANS ONE PEAK
/************************************************************************
* PREPARE PEAKS FOLLOWING THE COMMAND BOX................
*/
//default PEAK types
pk[0]=&gauss1;
pk[1]=&gauss2;
pk[2]=&gauss3;
pk[3]=&gauss4;
pk[4]=&gauss5;
pk[5]=&gauss6;
int maxi=spk.Length();
if (maxi>npeaks){maxi=npeaks;}
for (int i=0;i<maxi;i++){
if (spk[i]=='n'){
pk[i]=pk_dicto[1][i];//novosibirsk
printf("PEAK #%d ... Novosibirsk\n", i );
}else if(spk[i]=='b'){
pk[i]=pk_dicto[2][i];//BreitWiegner
printf("PEAK #%d ... BreitWigner\n", i );
}else if(spk[i]=='c'){
pk[i]=pk_dicto[3][i];//CBShape
printf("PEAK #%d ... CBShape\n", i );
}else if(spk[i]=='y'){
}else if(spk[i]=='z'){
}else{
pk[i]=pk_dicto[0][i]; //gauss
printf("PEAK #%d ... Gaussian\n", i );
}// ELSE CHAIN
}//i to maxi
for (int i=0;i<npeaks;i++){ printf("Peak %d at %f s=%f: PRINT:\n " , i, peak[i], sigm[i] );pk[i]->Print();}
/******************************************************** BACKGROUND pn-p4
* a0 == level - also skew
* a1 == p2
* a2 == p3
*/
// Build Chebychev polynomial p.d.f.
// RooRealVar a0("a0","a0", 0.) ;
RooRealVar a0("a0","a0", 0., -10, 10) ;
RooRealVar a1("a1","a1", 0., -10, 10) ;
RooRealVar a2("a2","a2", 0., -10, 10) ;
RooRealVar a3("a3","a3", 0., -10, 10) ;
RooArgSet setcheb;
if ( sbg=="pn" ){ setcheb.add(a0); a0=0.; a0.setConstant(kTRUE);bgarea=0.; bgarea.setConstant(kTRUE);}
if ( sbg=="p0" ){ setcheb.add(a0); a0=0.; a0.setConstant(kTRUE); }
if ( sbg=="p1" ){ setcheb.add(a0); }
if ( sbg=="p2" ){ setcheb.add(a1); setcheb.add(a0); }
if ( sbg=="p3" ){ setcheb.add(a2); setcheb.add(a1); setcheb.add(a0); }
if ( sbg=="p4" ){ setcheb.add(a3);setcheb.add(a2); setcheb.add(a1); setcheb.add(a0); }
// RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1,a2,a3) ) ;
RooChebychev bkg("bkg","Background",x, setcheb ) ;
/**********************************************************************
* MODEL
*/
RooArgList rl;
if (npeaks>0)rl.add( *pk[0] );
if (npeaks>1)rl.add( *pk[1] );
if (npeaks>2)rl.add( *pk[2] );
if (npeaks>3)rl.add( *pk[3] );
if (npeaks>4)rl.add( *pk[4] );
if (npeaks>5)rl.add( *pk[5] );
rl.add( bkg );
RooArgSet rs;
if (npeaks>0)rs.add( area1 );
if (npeaks>1)rs.add( area2 );
if (npeaks>2)rs.add( area3 );
if (npeaks>3)rs.add( area4 );
if (npeaks>4)rs.add( area5 );
if (npeaks>5)rs.add( area6 );
rs.add( bgarea );
RooAddPdf modelV("model","model", rl, rs );
/*
* WITH CUSTOMIZER - I can change parameters inside. But
* - then all is a clone and I dont know how to reach it
*/
RooCustomizer cust( modelV ,"cust");
/*
* Possibility to fix all sigma or tails....
*/
if (TPRegexp("scom").Match(command)!=0){//----------------------SCOM
printf("all sigma have common values.....\n%s", "");
if (npeaks>1)cust.replaceArg(sigma2,sigma1) ;
if (npeaks>2)cust.replaceArg(sigma3,sigma1) ;
if (npeaks>3)cust.replaceArg(sigma4,sigma1) ;
if (npeaks>4)cust.replaceArg(sigma5,sigma1) ;
if (npeaks>5)cust.replaceArg(sigma6,sigma1) ;
}
if (TPRegexp("tcom").Match(command)!=0){//----------------------TCOM
printf("all tails have common values.....\n%s", "");
if (npeaks>1)cust.replaceArg(tail2,tail1) ;
if (npeaks>2)cust.replaceArg(tail3,tail1) ;
if (npeaks>3)cust.replaceArg(tail4,tail1) ;
if (npeaks>4)cust.replaceArg(tail5,tail1) ;
if (npeaks>5)cust.replaceArg(tail6,tail1) ;
}
/* if (TPRegexp("tcom").Match(command)!=0){//----------------------TCOM Neni dalsi ACOM,NCOM pro CB...
printf("all tails have common values.....\n%s", "");
if (npeaks>1)cust.replaceArg(tail2,tail1) ;
if (npeaks>2)cust.replaceArg(tail3,tail1) ;
if (npeaks>3)cust.replaceArg(tail4,tail1) ;
if (npeaks>4)cust.replaceArg(tail5,tail1) ;
}
*/
if (TPRegexp("p1fix").Match(command)!=0){//----------------------
mean1.setConstant();printf("position 1 set constant%s\n","");
}
if (TPRegexp("p2fix").Match(command)!=0){//----------------------
mean2.setConstant();printf("position 2 set constant%s\n","");
}
if (TPRegexp("p3fix").Match(command)!=0){//----------------------
mean3.setConstant();printf("position 3 set constant%s\n","");
}
if (TPRegexp("p4fix").Match(command)!=0){//----------------------
mean4.setConstant();printf("position 4 set constant%s\n","");
}
if (TPRegexp("p5fix").Match(command)!=0){//----------------------
mean5.setConstant();printf("position 5 set constant%s\n","");
}
if (TPRegexp("p6fix").Match(command)!=0){//----------------------
mean6.setConstant();printf("position 6 set constant%s\n","");
}
if (TPRegexp("s1fix").Match(command)!=0){//----------------------
sigma1.setConstant();printf("sigma 1 set constant%s\n","");
}
if (TPRegexp("s2fix").Match(command)!=0){//----------------------
sigma2.setConstant();printf("sigma 2 set constant%s\n","");
}
if (TPRegexp("s3fix").Match(command)!=0){//----------------------
sigma3.setConstant();printf("sigma 3 set constant%s\n","");
}
if (TPRegexp("s4fix").Match(command)!=0){//----------------------
sigma4.setConstant();printf("sigma 4 set constant%s\n","");
}
if (TPRegexp("s5fix").Match(command)!=0){//----------------------
sigma5.setConstant();printf("sigma 5 set constant%s\n","");
}
if (TPRegexp("s6fix").Match(command)!=0){//----------------------
sigma6.setConstant();printf("sigma 6 set constant%s\n","");
}
RooAbsPdf* model = (RooAbsPdf*) cust.build(kTRUE) ; //build a clone...comment on changes...
// model->Print("t") ;
//delete model ; // eventualy delete the model...
/*
* DISPLAY RESULTS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
*/
TPad *orig_gpad=(TPad*)gPad;
TCanvas *c;
c=(TCanvas*)gROOT->GetListOfCanvases()->FindObject("fitresult");
if (c==NULL){
printf("making new canvas\n%s","");
c=new TCanvas("fitresult",h2->GetName(),1000,700);
}else{
printf("using old canvas\n%s","");
c->SetTitle( h2->GetName() );
}
c->Clear();
printf(" canvas cleared\n%s","");
c->Divide(1,2) ;
printf(" canvas divided\n%s","");
c->Modified();c->Update();
RooDataHist datah("datah","datah with x",x,h2);
RooPlot* xframe = x.frame();
datah.plotOn(xframe, DrawOption("logy") );
// return;
if (TPRegexp("chi2").Match(command)!=0){//----------------------CHI2
//from lorenzo moneta
// TH1 * h1 = datah.createHistogram(x);
// TF1 * f = model->asTF(RooArgList(x) , parameters ); //???
// h2->Fit(f);
//It will work but you need to create a THNSparse and fit it
//or use directly the ROOT::Fit::BinData class to create a ROOT::Fit::Chi2Function to minimize.
// THIS CANNOT DO ZERO BINS
fitresult = model->chi2FitTo( datah , Save() );
}else{
// FIT FIT FIT FIT FIT FIT FIT FIT FIT FIT
fitresult = model->fitTo( datah , Save() );
}
fitresult->SetTitle( h2->GetName() ); // I PUT histogram name to global fitresult
// will be done by printResult ... fitresult->Print("v") ;
//duplicite fitresult->floatParsFinal().Print("s") ;
// later - after parsfinale .... : printResult();
// model->Print(); // not interesting........
model->plotOn(xframe, LineColor(kRed), DrawOption("l0z") );
//,Minos(kFALSE)
/*
* Posledni nakreslena vec je vychodiskem pro xframe->resid...?
* NA PORADI ZALEZI....
*/
//unused RooHist* hresid = xframe->residHist() ;
RooHist* hpull = xframe->pullHist() ;
// RooPlot* xframe2 = x.frame(Title("Residual Distribution")) ;
// xframe2->addPlotable(hresid,"P") ;
// Construct a histogram with the pulls of the data w.r.t the curve
RooPlot* xframe3 = x.frame(Title("Pull Distribution")) ;
xframe3->addPlotable(hpull,"P") ;
/*
* plot components at the end.... PLOT >>>>>>>>>>>>>>>>
*/
int colorseq[10]={kRed,kGreen,kBlue,kYellow,kCyan,kMagenta,kViolet,kAzure,kGray,kOrange};
// RooArgSet* model_params = model->getParameters(x); // this returns all parameters
RooArgSet* model_params = model->getComponents();
TIterator* iter = model_params->createIterator() ;
RooAbsArg* arg ; int icomp=0, ipeak=0;
// printf("ENTERING COMPONENT ITERATOR x%dx.....................\n", icomp );
while((arg=(RooAbsArg*)iter->Next())) {
// printf("printing COMPONENT %d\n", icomp );
// arg->Print();
// printf("NAME==%s\n", arg->Class_Name() ); //This returns only RooAbsArg
// printf("NAME==%s\n", arg->ClassName() ); //This RooGaussian RooChebychev
if ( IsPeak( arg->ClassName() )==1 ){
pk[ipeak]=(RooAbsPdf*)arg; //?
// pk[ipeak]->Print();
ipeak++;
// printf("adresses ... %d - %d - %d\n", pk[0], pk[1], pk[2] );
}// yes peak.
icomp++;
}//iterations over all components
model->plotOn(xframe, Components(bkg), LineColor(kRed), LineStyle(kDashed), DrawOption("l0z") );
for (int i=0;i<npeaks;i++){
// printf("plotting %d. peak, color %d\n", i, colorseq[i+1] );
// printf("adresses ... %d - %d - %d\n", pk[0], pk[1], pk[2] );
// pk[i]->Print();
model->plotOn(xframe, Components( RooArgSet(*pk[i],bkg) ), LineColor(colorseq[i+1]), LineStyle(kDashed),
DrawOption("l0z") );
// DrawOption("pz"),DataError(RooAbsData::SumW2) );??? pz removes complains...warnings
// model.plotOn(xframe, Components( RooArgSet(*pk[i],bkg) ), LineColor(colorseq[i+1]), LineStyle(kDashed));
}
// WE SET THE 1st PAD in "fitresult" to LOGY.... 1
// ..... if the original window is LOGY..... :)
//
// printf("########### ORIGPAD LOGY==%d #########3\n", orig_gpad->GetLogy() );
c->cd(1); xframe->Draw(); gPad->SetLogy( orig_gpad->GetLogy() );
// c->cd(2); xframe2->Draw();
c->cd(2); xframe3->Draw();
c->Modified();c->Update();
orig_gpad->cd();
// printf("msat reference to peak 0 0 = %d, (%f)\n", msat[0][0] , msat[0][0]->getVal() );
for (int ii=0;ii<14;ii++){
for (int jj=0;jj<MAXPEAKS;jj++){
if ( msat[ii][jj]!=NULL){
msat_values[ii][jj]=msat[ii][jj]->getVal();
}//if
} //for for
}// for for
printf("at the total end of the constructor....%s\n","");
// done in pirntResult .. fitresult->floatParsFinal().Print("s") ;
printResult();
}; // constructor
int main(int argc, char **argv)
{
bool printSw = true;
//TString massModel = "Gauss-m[5622]";
string massModel = "DCB-m[5622]";
TString effbase = "/afs/cern.ch/user/p/pluca/work/Lb/Lmumu/results/";
bool printeff = false;
TString dodata = "data";
bool fitsingle = false;
TString wstr = "physRate_polp006";
TString decayToDo = "Lb2Lmumu";
if(dodata=="genMC") wstr += "_noDecay";
gROOT->ProcessLine(".x lhcbStyle.C");
RooRealVar * cosThetaL = new RooRealVar("cosThetaL","cosThetaL",0.,-1.,1.);
RooRealVar * cosThetaB = new RooRealVar("cosThetaB","cosThetaB",0.,-1.,1.);
RooRealVar * nsig_sw = new RooRealVar("nsig_sw","nsig_sw",1,-1.e6,1.e6);
RooRealVar * MCweight = new RooRealVar(wstr,wstr,1.,-1.e10,1.e10);
RooRealVar * MM = new RooRealVar("Lb_MassConsLambda","Lb_MassConsLambda",5620.,5500.,5900.);
TString datafilename = "/afs/cern.ch/user/p/pluca/work/Lb/Lmumu/candLb.root";
if(dodata=="MC") datafilename = "/afs/cern.ch/user/p/pluca/work/Lb/Lmumu/candLb_MC.root";
if(dodata=="genMC") datafilename = "/afs/cern.ch/work/p/pluca/weighted/Lmumu/"+(string)decayToDo+"_geomMC_Pythia8_NBweighted.root";
TreeReader * data;
if(dodata!="genMC") data = new TreeReader("cand"+decayToDo);
else data = new TreeReader("MCtree");
data->AddFile(datafilename);
TFile * histFile = new TFile("Afb_hist.root","recreate");
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR);
int nbins = 1;//CutsDef::nq2bins;
double q2min[] = {15.,11.0,15,16,18};//&CutsDef::q2min_highfirst[0];
double q2max[] = {20.,12.5,16,18,20};//&CutsDef::q2max_highfirst[0];
//int nbins = CutsDef::nq2bins
//double *q2min = &CutsDef::q2min[0];
//double *q2max = &CutsDef::q2max[0];
TGraphErrors * Afb_vs_q2 = new TGraphErrors();
TGraphErrors * AfbB_vs_q2 = new TGraphErrors();
TGraphErrors * fL_vs_q2 = new TGraphErrors();
TCanvas * ceff = new TCanvas();
RooCategory * samples = new RooCategory("samples","samples");
samples->defineType("DD");
samples->defineType("LL");
RooRealVar * afb = new RooRealVar("afb","afb",0.,-100,100);
RooRealVar * fL = new RooRealVar("fL","fL",0.7,-1.,10.);
//RooRealVar * afb = new RooRealVar("afb","afb",0.,-1.,1.);
//RooRealVar * fL = new RooRealVar("fL","fL",0.7,0.,1.);
RooRealVar * origafb = new RooRealVar("afb","afb",0.,-1.,1.);
RooRealVar * origfL = new RooRealVar("fL","fL",0.7,-1.,10.);
TString afbLpdf = "((3./8.)*(1.-fL)*(1 + TMath::Power(cosThetaL,2)) + afb*cosThetaL + (3./4.)*fL*(1 - TMath::Power(cosThetaL,2)))";
RooRealVar * afbB = new RooRealVar("afbB","afbB",0.,-100,100);
//RooRealVar * afbB = new RooRealVar("afbB","afbB",0.,-1.,1.);
RooRealVar * origafbB = new RooRealVar("afbB","afbB",0.,-1.,1.);
TString afbBpdf = "(1 + 2*afbB*cosThetaB)";
vector< vector< double > > afb_errs, afbB_errs, fL_errs;
TList * LLlist = new TList, * DDlist = new TList;
TCanvas * cDD = new TCanvas();
TCanvas * cLL = new TCanvas();
TCanvas * cDDB = new TCanvas();
TCanvas * cLLB = new TCanvas();
for(int i = 0; i < nbins; i++)
{
//if(q2min[i] < 8) continue;
TString q2name = ((TString)Form("q2_%4.2f_%4.2f",q2min[i],q2max[i])).ReplaceAll(".","");
TString curq2cut = Form("TMath::Power(J_psi_1S_MM/1000,2) >= %e && TMath::Power(J_psi_1S_MM/1000,2) < %e",q2min[i],q2max[i]);
//TString curq2cut = Form("TMath::Power(J_psi_1S_MM/1000,2) >= %e && TMath::Power(J_psi_1S_MM/1000,2) < %e && (Lb_MassConsLambda > 5680 || Lb_MassConsLambda < 5590)",q2min[i],q2max[i]);
cout << "------------------- q2 bin: " << q2min[i] << " - " << q2max[i] << " -----------------------" << endl;
TFile * effFile = NULL;
TH1F * effDD = NULL, * effLL = NULL, * effLLB = NULL, * effDDB = NULL;
if(q2min[i] == 15 && q2max[i] == 20)
{
effFile = TFile::Open(effbase+"LbeffvscosThetaL_DD.root");
effDD = (TH1F *)effFile->Get("htoteff");
effFile = TFile::Open(effbase+"LbeffvscosThetaL_LL.root");
effLL = (TH1F *)effFile->Get("htoteff");
effFile = TFile::Open(effbase+"LbeffvscosThetaB_DD.root");
effDDB = (TH1F *)effFile->Get("htot_nodet_eff");
effFile = TFile::Open(effbase+"LbeffvscosThetaB_LL.root");
effLLB = (TH1F *)effFile->Get("htot_nodet_eff");
}
else
{
effFile = TFile::Open(effbase+"Lbeff2D_cosThetaL_vs_q2_DD.root");
TH2F * effDD2D = (TH2F *)effFile->Get("htot_eff");
effDD = (TH1F*)GetSliceX(effDD2D,(q2max[i]+q2min[i])/2.);
effFile = TFile::Open(effbase+"Lbeff2D_cosThetaL_vs_q2_LL.root");
TH2F * effLL2D = (TH2F *)effFile->Get("htot_eff");
effLL = (TH1F*)GetSliceX(effLL2D,(q2max[i]+q2min[i])/2.);
effFile = TFile::Open(effbase+"Lbeff2D_cosThetaB_vs_q2_DD.root");
TH2F * effDDB2D = (TH2F *)effFile->Get("hupper_eff");
effDDB = (TH1F*)GetSliceX(effDDB2D,(q2max[i]+q2min[i])/2.);
effFile = TFile::Open(effbase+"Lbeff2D_cosThetaB_vs_q2_LL.root");
TH2F * effLLB2D = (TH2F *)effFile->Get("hupper_eff");
effLLB = (TH1F*)GetSliceX(effLLB2D,(q2max[i]+q2min[i])/2.);
}
ceff->cd();
/** FIT EFFICIENCY **/
RooDataHist * hLL = new RooDataHist("hLL","hLL",*cosThetaL,effLL);
RooDataHist * hDD = new RooDataHist("hDD","hDD",*cosThetaL,effDD);
RooRealVar * c1LL = new RooRealVar("c1LL","",0.,-1.,1);
RooRealVar * c1DD = new RooRealVar("c1DD","",0.,-1.,1);
RooRealVar * c2LL = new RooRealVar("c2LL","",0.,-1.,1);
RooRealVar * c2DD = new RooRealVar("c2DD","",0.,-1.,1);
TString effLLstr = "(1 + c1LL*cosThetaL + c2LL*TMath::Power(cosThetaL,2))";
TString effDDstr = "(1 + c1DD*cosThetaL + c2DD*TMath::Power(cosThetaL,2))";
RooAbsPdf * effLLpdf = new RooGenericPdf("effLLpdf", "", effLLstr, RooArgSet(*cosThetaL, *c1LL, *c2LL));
RooAbsPdf * effDDpdf = new RooGenericPdf("effDDpdf", "", effDDstr, RooArgSet(*cosThetaL, *c1DD, *c2DD));
effLLpdf->fitTo(*hLL,PrintLevel(-1));
effDDpdf->fitTo(*hDD,PrintLevel(-1));
fixParams(effLLpdf,cosThetaL);
fixParams(effDDpdf,cosThetaL);
RooDataHist * hLLB = new RooDataHist("hLLB","hLLB",*cosThetaB,effLLB);
RooDataHist * hDDB = new RooDataHist("hDDB","hDDB",*cosThetaB,effDDB);
RooRealVar * cB1LL = new RooRealVar("cB1LL","",0,-1.,1);
RooRealVar * cB1DD = new RooRealVar("cB1DD","",0,-1.,1);
RooRealVar * cB2LL = new RooRealVar("cB2LL","",0,-1.,1);
RooRealVar * cB2DD = new RooRealVar("cB2DD","",0,-1.,1);
TString effLLBstr = "(1 + cB1LL*cosThetaB + cB2LL*TMath::Power(cosThetaB,2))";
TString effDDBstr = "(1 + cB1DD*cosThetaB + cB2DD*TMath::Power(cosThetaB,2))";
RooAbsPdf * effLLpdfB = new RooGenericPdf("effLLpdfB", "", effLLBstr, RooArgSet(*cosThetaB, *cB1LL, *cB2LL));
RooAbsPdf * effDDpdfB = new RooGenericPdf("effDDpdfB", "", effDDBstr, RooArgSet(*cosThetaB, *cB1DD, *cB2DD));
effLLpdfB->fitTo(*hLLB,PrintLevel(-1));
effDDpdfB->fitTo(*hDDB,PrintLevel(-1));
fixParams(effLLpdfB,cosThetaB);
fixParams(effDDpdfB,cosThetaB);
//cout << q2min[i] << " - " << q2max[i] << " LL cosThetaL -> " << c1LL->getVal() << " " << c2LL->getVal() << endl;
//cout << q2min[i] << " - " << q2max[i] << " DD cosThetaL -> " << c1DD->getVal() << " " << c2DD->getVal() << endl;
//cout << q2min[i] << " - " << q2max[i] << " LL cosThetaB -> " << cB1LL->getVal() << " " << cB2LL->getVal() << endl;
//cout << q2min[i] << " - " << q2max[i] << " DD cosThetaB -> " << cB1DD->getVal() << " " << cB2DD->getVal() << endl;
if(printeff) {
GetFrame(cosThetaL, hLL,effLLpdf,"-nochi2",0,NULL,0,"cos#theta_{l}","Tot. eff.")->Draw();
ceff->Print("DDeffFit"+q2name+".pdf");
GetFrame(cosThetaL, hDD,effDDpdf,"-nochi2",0,NULL,0,"cos#theta_{l}","Tot. eff.")->Draw();
ceff->Print("LLeffFit"+q2name+".pdf");
GetFrame(cosThetaB, hLLB,effLLpdfB,"-nochi2",0,NULL,0,"cos#theta_{#Lambda}","Tot. eff.")->Draw();
ceff->Print("DDeffFitB"+q2name+".pdf");
GetFrame(cosThetaB, hDDB,effDDpdfB,"-nochi2",0,NULL,0,"cos#theta_{#Lambda}","Tot. eff.")->Draw();
ceff->Print("LLeffFitB"+q2name+".pdf"); }
/** FIT AFB **/
afb->setVal(0);
afbB->setVal(0);
fL->setVal(0.7);
TString LLnorm = "1./( 1. + (2./3.)*afb*c1LL + (2./5.)*c2LL - (1./5.)*c2LL*fL )*"+effLLstr;
TString DDnorm = "1./( 1. + (2./3.)*afb*c1DD + (2./5.)*c2DD - (1./5.)*c2DD*fL )*"+effDDstr;
RooAbsPdf * corrPdfLL = new RooGenericPdf(Form("corrPdfLL_%i",i),LLnorm+"*"+afbLpdf,RooArgSet(*cosThetaL, *afb, *fL, *c1LL, *c2LL) );
RooAbsPdf * corrPdfDD = new RooGenericPdf(Form("corrPdfDD_%i",i),DDnorm+"*"+afbLpdf,RooArgSet(*cosThetaL, *afb, *fL, *c1DD, *c2DD) );
TString LLnormB = "1./( (2./3.)*( 2*afbB*cB1LL + cB2LL + 3.) )*"+effLLBstr;
TString DDnormB = "1./( (2./3.)*( 2*afbB*cB1DD + cB2DD + 3.) )*"+effDDBstr;
RooAbsPdf * corrPdfLLB = new RooGenericPdf(Form("corrPdfLLB_%i",i),LLnormB+"*"+afbBpdf,RooArgSet(*cosThetaB, *afbB, *cB1LL, *cB2LL) );
RooAbsPdf * corrPdfDDB = new RooGenericPdf(Form("corrPdfDDB_%i",i),DDnormB+"*"+afbBpdf,RooArgSet(*cosThetaB, *afbB, *cB1DD, *cB2DD) );
TCut cutLL = CutsDef::LLcut + (TCut)curq2cut;
TCut cutDD = CutsDef::DDcut + (TCut)curq2cut;
if(dodata=="genMC")
{
corrPdfLLB = new RooGenericPdf("corrPdfLL",afbBpdf,RooArgSet(*cosThetaB, *afbB, *cB1LL, *cB2LL) );
corrPdfDDB = new RooGenericPdf("corrPdfDD",afbBpdf,RooArgSet(*cosThetaB, *afbB, *cB1DD, *cB2DD) );
corrPdfLL = new RooGenericPdf("corrPdfLL",afbLpdf,RooArgSet(*cosThetaL, *afb, *fL, *c1LL, *c2LL) );
corrPdfDD = new RooGenericPdf("corrPdfDD",afbLpdf,RooArgSet(*cosThetaL, *afb, *fL, *c1DD, *c2DD) );
cutLL = (TCut)curq2cut;
cutDD = (TCut)curq2cut;
}
Analysis * anaLL = new Analysis(Form("LL_mass_%i",i),"Lb",data,&cutLL,MM);
anaLL->AddVariable(cosThetaL);
anaLL->AddVariable(cosThetaB);
anaLL->AddVariable("J_psi_1S_MM");
if(dodata!="data") anaLL->SetWeight(wstr);
RooDataSet * dataLL = anaLL->GetDataSet("-recalc-docuts");
Analysis * anaDD = new Analysis(Form("DD_mass_%i",i),"Lb",data,&cutDD,MM);
anaDD->AddVariable(cosThetaL);
anaDD->AddVariable(cosThetaB);
anaDD->AddVariable("J_psi_1S_MM");
if(dodata!="data") anaDD->SetWeight(wstr);
RooDataSet * dataDD = anaDD->GetDataSet("-recalc-docuts");
RooDataSet * sdataDD, * sdataLL;
if(dodata=="data")
{
sdataLL = anaLL->CalcSweight("",massModel.c_str(),"Exp");
if(printSw) {
GetFrame(MM,NULL,sdataLL,"-nochi2",30,NULL,0,"M(#Lambda#mu#mu) (MeV/c^{2})")->Draw();
ceff->Print("Mass_LL_sWeighted"+q2name+".pdf");
GetFrame(cosThetaL,NULL,sdataLL,"-nochi2",6,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("cosThetaL_LL_sWeighted"+q2name+".pdf");
GetFrame(cosThetaL,NULL,dataLL,"-nochi2",6,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("cosThetaL_LL_"+q2name+".pdf");
}
sdataDD = anaDD->CalcSweight("",massModel.c_str(),"Exp");
if(printSw) {
GetFrame(MM,NULL,sdataDD,"-nochi2",30,NULL,0,"M(#Lambda#mu#mu) (MeV/c^{2})")->Draw();
ceff->Print("Mass_DD_sWeighted"+q2name+".pdf");
GetFrame(cosThetaL,NULL,sdataDD,"-nochi2",10,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("cosThetaL_DD_sWeighted"+q2name+".pdf");
GetFrame(cosThetaL,NULL,dataDD,"-nochi2",10,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("cosThetaL_DD_"+q2name+".pdf");
}
}
else { sdataLL = dataLL; sdataDD = dataDD; }
histFile->cd();
TTree * LLTree = (TTree*)sdataLL->tree();
LLTree->SetName(Form("treeLL_%i",i));
LLlist->Add(LLTree);
TTree * DDTree = (TTree*)sdataDD->tree();
DDTree->SetName(Form("treeDD_%i",i));
DDlist->Add(DDTree);
// CREATE COMBINED DATASET
RooDataSet * combData;
if(dodata=="data") combData = new RooDataSet(Form("combData_%i",i),"combined data",RooArgSet(*cosThetaL,*cosThetaB,*nsig_sw),Index(*samples),Import("DD",*sdataDD),Import("LL",*sdataLL),WeightVar("nsig_sw"));
else combData = new RooDataSet(Form("combData_%i",i),"combined data",RooArgSet(*cosThetaL,*cosThetaB,*MCweight),Index(*samples),Import("DD",*sdataDD),Import("LL",*sdataLL),WeightVar(wstr));
// FIT COS LEPTON
RooSimultaneous * combModel = new RooSimultaneous(Form("combModel_%i",i),"",*samples);
combModel->addPdf(*corrPdfLL,"LL");
combModel->addPdf(*corrPdfDD,"DD");
combModel->fitTo(*combData,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
if(fitsingle) corrPdfLL->fitTo(*sdataLL,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
GetFrame(cosThetaL,corrPdfLL,sdataLL,"-sumW2err-nochi2-noCost",6,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("Afb_LL_"+q2name+".pdf");
if(fitsingle) corrPdfDD->fitTo(*sdataDD,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
GetFrame(cosThetaL,corrPdfDD,sdataDD,"-sumW2err-nochi2-noCost",10,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("Afb_DD_"+q2name+".pdf");
Afb_vs_q2->SetPoint(i,(q2max[i] + q2min[i])/2.,afb->getVal());
Afb_vs_q2->SetPointError(i,(q2max[i] - q2min[i])/2.,afb->getError());
fL_vs_q2->SetPoint(i,(q2max[i] + q2min[i])/2.,fL->getVal());
fL_vs_q2->SetPointError(i,(q2max[i] - q2min[i])/2.,fL->getError());
// FIT COS HADRON
RooSimultaneous * combModelB = new RooSimultaneous(Form("combModelB_%i",i),"",*samples);
combModelB->addPdf(*corrPdfLLB,"LL");
combModelB->addPdf(*corrPdfDDB,"DD");
combModelB->fitTo(*combData,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
if(fitsingle) corrPdfLLB->fitTo(*sdataLL,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
GetFrame(cosThetaB,corrPdfLLB,sdataLL,"-sumW2err-nochi2-noCost",6,NULL,0,"cos#theta_{#Lambda}")->Draw();
ceff->Print("AfbB_LL_"+q2name+".pdf");
if(fitsingle) corrPdfDDB->fitTo(*sdataDD,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
GetFrame(cosThetaB,corrPdfDDB,sdataDD,"-sumW2err-nochi2-noCost",10,NULL,0,"cos#theta_{#Lambda}")->Draw();
ceff->Print("AfbB_DD_"+q2name+".pdf");
AfbB_vs_q2->SetPoint(i,(q2max[i] + q2min[i])/2.,afbB->getVal());
AfbB_vs_q2->SetPointError(i,(q2max[i] - q2min[i])/2.,afbB->getError());
cout << endl << fixed << setprecision(6) << "AfbB = " << afbB->getVal() << " +/- " << afbB->getError() << endl;
cout << "Afb = " << afb->getVal() << " +/- " << afb->getError() << endl;
cout << "fL = " << fL->getVal() << " +/- " << fL->getError() << endl;
cout << endl;
cout << "------------------------ FELDMAN AND COUSINS ------------------------" << endl;
vector < RooDataSet * > datas;
vector < RooAbsPdf * > pdfs, pdfsB;
vector < TString > cat;
cat.push_back("LL");
cat.push_back("DD");
datas.push_back(sdataLL);
datas.push_back(sdataDD);
RooArgSet * origPars = new RooArgSet();
origPars->add(*origafb);
origPars->add(*origfL);
pdfs.push_back(corrPdfLL);
pdfs.push_back(corrPdfDD);
vector< double > afb_err, afbB_err, fL_err;
/*
double fLval = fL->getVal(), fLerr = fL->getError();
FeldmanCousins * FC = new FeldmanCousins(q2name,cat,datas,pdfs,cosThetaL,afb,"nsig_sw");
//FC->SetNPointsToScan(20);
//FC->SetNExp(1000);
if(q2min[i]==18) afb_err = FC->ExtractLimits(origPars,-0.3,0.3);
else if( (afb->getVal()-1.4*afb->getError()) > -1 && (afb->getVal()+1.4*afb->getError()) < 1 )
afb_err = FC->ExtractLimits(origPars,afb->getVal()-1.4*afb->getError(),afb->getVal()+1.4*afb->getError());
else afb_err = FC->ExtractLimits(origPars,-0.4,0.4);
//FeldmanCousins * FCfL = new FeldmanCousins(q2name,cat,datas,pdfs,cosThetaL,fL,"nsig_sw");
//if(q2min[i]==11) fL_err = FCfL->ExtractLimits(origPars,0.,0.6);
//else if (q2min[i]==18) fL_err = FCfL->ExtractLimits(origPars,0.75,0.992);
//( (fLval-1.3*fLerr) > 0 && (fLval+1.3*fLerr) <= 1 )
//else fL_err = FCfL->ExtractLimits(origPars,fLval-1.3*fLerr,fLval+1.3*fLerr);
afb_errs.push_back(afb_err);
//fL_errs.push_back(fL_err);
RooArgSet * origParsB = new RooArgSet();
origParsB->add(*origafbB);
pdfsB.push_back(corrPdfLLB);
pdfsB.push_back(corrPdfDDB);
FeldmanCousins * FCB = new FeldmanCousins(q2name,cat,datas,pdfsB,cosThetaB,afbB,"nsig_sw");
if( (afbB->getVal()-1.5*afbB->getError()) > -1 && (afbB->getVal()+1.5*afbB->getError()) < 1 )
afbB_err = FCB->ExtractLimits(origParsB,afbB->getVal()-1.5*afbB->getError(),afbB->getVal()+1.5*afbB->getError());
else afbB_err = FCB->ExtractLimits(origParsB,-0.4,0.4);
afbB_errs.push_back(afbB_err);
*/
delete effDD;
delete effLL;
delete effLLB;
delete effDDB;
}
cDD->Print("DDeff.pdf");
cLL->Print("LLeff.pdf");
cDDB->Print("DDBeff.pdf");
cLLB->Print("LLBeff.pdf");
Afb_vs_q2->GetXaxis()->SetTitle("q^{2}");
Afb_vs_q2->GetYaxis()->SetTitle("Afb");
Afb_vs_q2->SetMaximum(1);
Afb_vs_q2->SetMinimum(-1);
Afb_vs_q2->Draw("AP");
ceff->Print("Afb_vs_q2.pdf");
AfbB_vs_q2->GetXaxis()->SetTitle("q^{2}");
AfbB_vs_q2->GetYaxis()->SetTitle("AfbB");
AfbB_vs_q2->SetMaximum(1);
AfbB_vs_q2->SetMinimum(-1);
AfbB_vs_q2->Draw("AP");
ceff->Print("AfbB_vs_q2.pdf");
fL_vs_q2->GetXaxis()->SetTitle("q^{2}");
fL_vs_q2->GetYaxis()->SetTitle("fL");
fL_vs_q2->Draw("AP");
ceff->Print("fL_vs_q2.pdf");
for(int bb = 0; bb < Afb_vs_q2->GetN(); bb++)
{
double qq, qqerr, afbv, afbBv, fLv;
Afb_vs_q2->GetPoint(bb,qq,afbv);
qqerr = Afb_vs_q2->GetErrorX(bb);
AfbB_vs_q2->GetPoint(bb,qq,afbBv);
fL_vs_q2->GetPoint(bb,qq,fLv);
cout << fixed << setprecision(1) << qq-qqerr << " - " << qq+qqerr;
cout << fixed << setprecision(4);
//cout << " & $" << afbv << "_{-" << TMath::Abs(afb_errs[bb][0] - afbv) << "}^{+" << TMath::Abs(afb_errs[bb][1] - afbv) << "} \\text{(stat)} \\pm \\text{(sys)}$ ";
//cout << " & $" << afbBv << "_{-" << TMath::Abs(afbB_errs[bb][0] - afbBv) << "}^{+" << TMath::Abs(afbB_errs[bb][1]-afbBv) << "} \\text{(stat)} \\pm \\text{(sys)}$ " ;
//cout << " & $" << fLv << "_{-" << TMath::Abs(fL_errs[bb][0] - fLv) << "}^{+" << TMath::Abs(fL_errs[bb][1] - fLv) << "} \\text{(stat)} \\pm \\text{(sys)}$ ";
cout << " \\\\ " << endl;
}
histFile->cd();
TTree * finalLLtree = (TTree*)TTree::MergeTrees(LLlist);
TTree * finalDDtree = (TTree*)TTree::MergeTrees(DDlist);
finalLLtree->SetName("LL_data");
finalDDtree->SetName("DD_data");
finalLLtree->Write();
finalDDtree->Write();
delete ceff;
histFile->Write();
delete histFile;
}
void rf104_classfactory()
{
// W r i t e c l a s s s k e l e t o n c o d e
// --------------------------------------------------
// Write skeleton p.d.f class with variable x,a,b
// To use this class,
// - Edit the file MyPdfV1.cxx and implement the evaluate() method in terms of x,a and b
// - Compile and link class with '.x MyPdfV1.cxx+'
//
RooClassFactory::makePdf("MyPdfV1","x,A,B") ;
// W i t h a d d e d i n i t i a l v a l u e e x p r e s s i o n
// ---------------------------------------------------------------------
// Write skeleton p.d.f class with variable x,a,b and given formula expression
// To use this class,
// - Compile and link class with '.x MyPdfV2.cxx+'
//
RooClassFactory::makePdf("MyPdfV2","x,A,B","","A*fabs(x)+pow(x-B,2)") ;
// W i t h a d d e d a n a l y t i c a l i n t e g r a l e x p r e s s i o n
// ---------------------------------------------------------------------------------
// Write skeleton p.d.f class with variable x,a,b, given formula expression _and_
// given expression for analytical integral over x
// To use this class,
// - Compile and link class with '.x MyPdfV3.cxx+'
//
RooClassFactory::makePdf("MyPdfV3","x,A,B","","A*fabs(x)+pow(x-B,2)",kTRUE,kFALSE,
"x:(A/2)*(pow(x.max(rangeName),2)+pow(x.min(rangeName),2))+(1./3)*(pow(x.max(rangeName)-B,3)-pow(x.min(rangeName)-B,3))") ;
// U s e i n s t a n c e o f c r e a t e d c l a s s
// ---------------------------------------------------------
// Compile MyPdfV3 class (only when running in CINT)
gROOT->ProcessLineSync(".x MyPdfV3.cxx+") ;
// Create instance of MyPdfV3 class
RooRealVar a("a","a",1) ;
RooRealVar b("b","b",2,-10,10) ;
RooRealVar y("y","y",-10,10);
MyPdfV3 pdf("pdf","pdf",y,a,b) ;
// Generate toy data from pdf and plot data and p.d.f on frame
RooPlot* frame1 = y.frame(Title("Compiled class MyPdfV3")) ;
RooDataSet* data = pdf.generate(y,1000) ;
pdf.fitTo(*data) ;
data->plotOn(frame1) ;
pdf.plotOn(frame1) ;
// -----------------------------------------------------------------
// C o m p i l e d v e r s i o n o f e x a m p l e r f 1 0 3
// =================================================================
// Declare observable x
RooRealVar x("x","x",-20,20) ;
// The RooClassFactory::makePdfInstance() function performs code writing, compiling, linking
// and object instantiation in one go and can serve as a straight replacement of RooGenericPdf
RooRealVar alpha("alpha","alpha",5,0.1,10) ;
RooAbsPdf* genpdf = RooClassFactory::makePdfInstance("GenPdf","(1+0.1*fabs(x)+sin(sqrt(fabs(x*alpha+0.1))))",RooArgSet(x,alpha)) ;
// Generate a toy dataset from the interpreted p.d.f
RooDataSet* data2 = genpdf->generate(x,50000) ;
// Fit the interpreted p.d.f to the generated data
genpdf->fitTo(*data2) ;
// Make a plot of the data and the p.d.f overlaid
RooPlot* frame2 = x.frame(Title("Compiled version of pdf of rf103")) ;
data2->plotOn(frame2) ;
genpdf->plotOn(frame2) ;
// Draw all frames on a canvas
TCanvas* c = new TCanvas("rf104_classfactory","rf104_classfactory",800,400) ;
c->Divide(2) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
}
void MakePlots(RooWorkspace* ws){
// Here we make plots of the discriminating variable (invMass) after the fit
// and of the control variable (isolation) after unfolding with sPlot.
std::cout << "make plots" << std::endl;
// make our canvas
TCanvas* cdata = new TCanvas("sPlot","sPlot demo", 400, 600);
cdata->Divide(1,3);
// get what we need out of the workspace
RooAbsPdf* model = ws->pdf("model");
RooAbsPdf* zModel = ws->pdf("zModel");
RooAbsPdf* qcdModel = ws->pdf("qcdModel");
RooRealVar* isolation = ws->var("isolation");
RooRealVar* invMass = ws->var("invMass");
// note, we get the dataset with sWeights
RooDataSet* data = (RooDataSet*) ws->data("dataWithSWeights");
// this shouldn't be necessary, need to fix something with workspace
// do this to set parameters back to their fitted values.
model->fitTo(*data, Extended() );
//plot invMass for data with full model and individual componenets overlayed
// TCanvas* cdata = new TCanvas();
cdata->cd(1);
RooPlot* frame = invMass->frame() ;
data->plotOn(frame ) ;
model->plotOn(frame) ;
model->plotOn(frame,Components(*zModel),LineStyle(kDashed), LineColor(kRed)) ;
model->plotOn(frame,Components(*qcdModel),LineStyle(kDashed),LineColor(kGreen)) ;
frame->SetTitle("Fit of model to discriminating variable");
frame->Draw() ;
// Now use the sWeights to show isolation distribution for Z and QCD.
// The SPlot class can make this easier, but here we demonstrait in more
// detail how the sWeights are used. The SPlot class should make this
// very easy and needs some more development.
// Plot isolation for Z component.
// Do this by plotting all events weighted by the sWeight for the Z component.
// The SPlot class adds a new variable that has the name of the corresponding
// yield + "_sw".
cdata->cd(2);
// create weightfed data set
RooDataSet * dataw_z = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"zYield_sw") ;
RooPlot* frame2 = isolation->frame() ;
dataw_z->plotOn(frame2, DataError(RooAbsData::SumW2) ) ;
frame2->SetTitle("isolation distribution for Z");
frame2->Draw() ;
// Plot isolation for QCD component.
// Eg. plot all events weighted by the sWeight for the QCD component.
// The SPlot class adds a new variable that has the name of the corresponding
// yield + "_sw".
cdata->cd(3);
RooDataSet * dataw_qcd = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"qcdYield_sw") ;
RooPlot* frame3 = isolation->frame() ;
dataw_qcd->plotOn(frame3,DataError(RooAbsData::SumW2) ) ;
frame3->SetTitle("isolation distribution for QCD");
frame3->Draw() ;
// cdata->SaveAs("SPlot.gif");
}
void statTest(double mu_pe, double mu_hyp, ModelConfig *mc , RooDataSet *data ){
int nToyMC = 5;
// set roofit seed
RooRandom::randomGenerator()->SetSeed();
cout << endl;
cout << endl;
cout << "Will generate " << nToyMC << " pseudo-experiments for : " << endl;
cout << " - mu[pseudo-data] = " << mu_pe << endl;
cout << " - mu[stat-test] = " << mu_hyp << endl;
cout << endl;
// Check number of POI (for Wald approx)
RooArgSet *ParamOfInterest = (RooArgSet*) mc->GetParametersOfInterest();
int nPOI = ParamOfInterest->getSize();
if(nPOI>1){
cout <<"not sure what to do with other parameters of interest, but here are their values"<<endl;
mc->GetParametersOfInterest()->Print("v");
}
RooRealVar* firstPOI = (RooRealVar*) ParamOfInterest->first();
RooAbsPdf *simPdf = (mc->GetPdf());
//PrintAllParametersAndValues( *mc->GetGlobalObservables() );
//PrintAllParametersAndValues( *mc->GetObservables() );
firstPOI->setVal(0.0); // FIXME
//simPdf->fitTo( *data, Hesse(kTRUE), Minos(kTRUE), PrintLevel(1) );
simPdf->fitTo( *data );
// set up the sampler
ToyMCSampler sampler;
sampler.SetPdf(*mc->GetPdf());
sampler.SetObservables(*mc->GetObservables());
sampler.SetNToys(nToyMC);
sampler.SetGlobalObservables(*mc->GetGlobalObservables());
sampler.SetParametersForTestStat(*mc->GetParametersOfInterest());
RooArgSet* poiset = dynamic_cast<RooArgSet*>(ParamOfInterest->Clone());
// only unconditional fit
MinNLLTestStat *minNll = new MinNLLTestStat(*mc->GetPdf());
minNll->EnableDetailedOutput(true);
sampler.AddTestStatistic(minNll);
// enable PROOF if desired
//ProofConfig pc(*w, 8, "workers=8", kFALSE);
//sampler.SetProofConfig(&pc);
// evaluate the test statistics - this is where most of our time will be spent
cout << "Generating " << nToyMC << " toys...this will take a few minutes" << endl;
TStopwatch *mn_t = new TStopwatch;
mn_t->Start();
RooDataSet* sd = sampler.GetSamplingDistributions(*poiset);
cout << "Toy generation complete :" << endl;
// stop timing
mn_t->Stop();
cout << " total CPU time: " << mn_t->CpuTime() << endl;
cout << " total real time: " << mn_t->RealTime() << endl;
// now sd contains all information about our test statistics, including detailed output
// we might eg. want to explore the results either directly, or first converting to a TTree
// do the conversion
TFile f("mytoys.root", "RECREATE");
TTree *toyTree = RooStats::GetAsTTree("toyTree", "TTree created from test statistics", *sd);
// save result to file, but in general do whatever you like
f.cd();
toyTree->Write();
f.Close();
/*
TFile* tmpFile = new TFile("mytoys.root","READ");
TTree* myTree = (TTree*)tmpFile->Get("toyTree");
// get boundaries for histograms
TIter nextLeaf( (myTree->GetListOfLeaves())->MakeIterator() );
TObject* leafObj(0);
map<TString, float> xMaxs;
map<TString, float> xMins;
for(int i(0); i<myTree->GetEntries(); i++) {
myTree->GetEntry(i);
nextLeaf = ( (myTree->GetListOfLeaves())->MakeIterator() );
while( (leafObj = nextLeaf.Next()) ) {
TString name(leafObj->GetName());
float value(myTree->GetLeaf( leafObj->GetName() )->GetValue());
if(value > xMaxs[name]) { xMaxs[name] = value; }
if(value < xMins[name]) { xMins[name] = value; }
} // loop over leaves
} // loop over tree entries
// plot everything in the tree
myTree->GetEntry(0);
nextLeaf = ( (myTree->GetListOfLeaves())->MakeIterator() );
leafObj = 0;
// make a histogram per leaf
map<TString, TH1F*> hists;
myTree->GetEntry(0);
while( (leafObj = nextLeaf.Next()) ) {
if(!leafObj) { continue; }
//cout << leafObj->GetName() << endl;
TString name(leafObj->GetName());
// special ones : fit related things
if(name.Contains("covQual")) { hists[name] = new TH1F(name,name,5,0,5); continue; }
if(name.Contains("fitStatus")) { hists[name] = new TH1F(name,name,5,0,5); continue; }
int nbin(500);
float histMin( xMins[name] - 0.1*fabs(xMins[name]) );
float histMax( xMaxs[name] + 0.1*fabs(xMaxs[name]) );
if(name.Contains("ATLAS_norm")) { // floating normalization factors
histMin = 0; histMax = 10;
}
else if(name.Contains("gamma_stat")) { // statistical nus param
if(name.Contains("globObs")) { // get custom range for sampling
histMin = int( xMins[name] - 0.1*fabs(xMins[name]) );
histMax = int( xMaxs[name] + 0.1*fabs(xMaxs[name]) );
} // use small range for pull and error
else { nbin = 100; histMin = 0.0; histMax = 2.0; }
}
else if(name.Contains("_err")) { // errors on nus param
nbin = 100; histMin = 0.0; histMax = 2.0;
}
else if(name.Contains("fitCond") || name.Contains("fitUncond") || name.Contains("globObs")) { // fit pulls
nbin = 500; histMin = -5; histMax = 5;
}
hists[name] = new TH1F(name,name,nbin,histMin,histMax);
} // loop over leaves to declare histos
// loop over entries and fill histograms
for(int i(0); i<myTree->GetEntries(); i++) {
myTree->GetEntry(i);
nextLeaf = ( (myTree->GetListOfLeaves())->MakeIterator() );
while( (leafObj = nextLeaf.Next()) ) {
TString name(leafObj->GetName());
if(hists.find(name) == hists.end()) { continue; }
hists[name]->Fill( myTree->GetLeaf( leafObj->GetName() )->GetValue() );
} // loop over leaves
} // loop over tree entries
// overflow and underflow
for(map<TString,TH1F*>::iterator ihist(hists.begin()); ihist!=hists.end(); ihist++) {
if(ihist->second->GetBinContent(0)>0) {
ihist->second->SetBinContent(1, ihist->second->GetBinContent(0) + ihist->second->GetBinContent(1) );
// fix err
}
int nBinx = ihist->second->GetNbinsX();
if(ihist->second->GetBinContent(nBinx)>0) {
ihist->second->SetBinContent(nBinx-1, ihist->second->GetBinContent(nBinx) + ihist->second->GetBinContent(nBinx-1) );
// fix err
}
}
// save the results
TString dirName(OutputDir+"/PlotsStatisticalTest/GlobalFit");
if(drawPlots) {
system(TString("mkdir -vp "+dirName));
}
TCanvas* canvas = new TCanvas("pulls");
TLegend *leg = new TLegend(0.67, 0.64, 0.87, 0.86);
LegendStyle(leg);
for(map<TString,TH1F*>::iterator ihist(hists.begin()); ihist!=hists.end(); ihist++) {
if( (ihist->first).Contains("fitCond_") ) { continue; } // skip unconditional fit - get it explicitly
canvas->Clear();
leg->Clear();
TString niceName(ihist->first);
niceName.ReplaceAll("fitUncond_","");
//niceName.ReplaceAll("SD_TS0_",""); // not good if have multiple test statistics
// conditional fit information
ihist->second->SetLineColor(kGray+2);
ihist->second->SetTitle(niceName);
ihist->second->SetLineStyle(kSolid);
ihist->second->SetLineWidth(2);
if((ihist->first).Contains("fit") && !(ihist->first).Contains("_err")
&& !(ihist->first).Contains("Qual") && !(ihist->first).Contains("Status")) {
ihist->second->Rebin(4);
}
// ihist->second->GetXaxis()->SetTitle("");
// ihist->second->GetYaxis()->SetTitle("");
if(niceName.Contains("globObs")) {
leg->AddEntry( ihist->second, "Sampling", "l" ); // add value of mu
} else {
leg->AddEntry( ihist->second, "Unconditional Fit", "l" ); // add value of mu
}
TString condName(ihist->first);
condName.ReplaceAll("fitUncond","fitCond");
// uncomditional fit information
if(hists.find(condName) != hists.end() && condName != ihist->first) {
hists[condName]->SetLineColor(kGray+2);
hists[condName]->SetLineStyle(kDashed);
hists[condName]->SetLineWidth(2);
if(!(ihist->first).Contains("_err")) { hists[condName]->Rebin(4); }
leg->AddEntry( hists[condName], "Conditional Fit", "l" );
if( hists[condName]->GetMaximum() > ihist->second->GetMaximum() ) {
ihist->second->SetMaximum( hists[condName]->GetMaximum() );
}
}
ihist->second->SetMaximum( 1.2 * ihist->second->GetMaximum() );
canvas->cd();
ihist->second->Draw();
leg->Draw();
if(hists[condName] && condName != ihist->first) { hists[condName]->Draw("same"); }
if(drawPlots) {
canvas->Print(dirName+"/"+niceName+".eps");
canvas->Print(dirName+"/"+niceName+".png");
}
MainDirStatTest->cd();
canvas->Write();
gROOT->cd();
}
*/
return;
}
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 DoSPlot(RooWorkspace* ws){
std::cout << "Calculate sWeights" << std::endl;
RooAbsPdf* model = ws->pdf("model");
RooRealVar* nsig = ws->var("nsig");
RooRealVar* nBbkg = ws->var("nBbkg");
RooRealVar* nbkg = ws->var("nbkg");
RooRealVar* nbkg2 = ws->var("nbkg2");
RooDataSet* data = (RooDataSet*) ws->data("data");
// fit the model to the data.
model->fitTo(*data, Extended() );
RooMsgService::instance().setSilentMode(true);
// Now we use the SPlot class to add SWeights to our data set
// based on our model and our yield variables
RooStats::SPlot* sData = new RooStats::SPlot("sData","An SPlot",
*data, model, RooArgList(*nsig,*nBbkg,*nbkg,*nbkg2) );
// Check that our weights have the desired properties
std::cout << "Check SWeights:" << std::endl;
std::cout << std::endl << "Yield of sig is "
<< nsig->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("nsig") << std::endl;
std::cout << std::endl << "Yield of Bbkg is "
<< nBbkg->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("nBbkg") << std::endl;
std::cout << std::endl << "Yield of bkg is "
<< nbkg->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("nbkg") << std::endl;
std::cout << std::endl << "Yield of bkg2 is "
<< nbkg2->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("nbkg2") << std::endl;
cout << endl; cout << endl; cout << endl;
float sum20=0;
float sum50=0;
float sum100=0;
float sum200=0;
float sum300=0;
float sum600=0;
float sum900=0;
float sum1200=0;
float total=0;
// saving weights into a file
ofstream myfile;
myfile.open ("weights.txt");
// plot the weight event by event with the Sum of events values as cross-check
for(Int_t i=0; i < data->numEntries(); i++) {
//myfile << sData->GetSWeight(i,"nsig") << " " << sData->GetSWeight(i,"nBbkg") << " " << sData->GetSWeight(i,"nbkg") << " " << sData->GetSWeight(i,"nbkg2") << endl;
//myfile << sData->GetSWeight(i,"nsig") <<endl;
myfile << (unsigned int) data->get(i)->getRealValue("run")
<< " " << (unsigned int) data->get(i)->getRealValue("event")
<< " " << (float) data->get(i)->getRealValue("FourMu_Mass")
<< " " << sData->GetSWeight(i,"nsig")
<< endl;
// std::cout << "nsig Weight " << sData->GetSWeight(i,"nsig")
// << " nBbkg Weight " << sData->GetSWeight(i,"nBbkg")
// << " nbkg Weight " << sData->GetSWeight(i,"nbkg")
// << " nbkg2 Weight " << sData->GetSWeight(i,"nbkg2")
// << " Total Weight " << sData->GetSumOfEventSWeight(i)
// << std::endl;
total+=sData->GetSWeight(i,"nsig");
if(i<20) sum20+=sData->GetSWeight(i,"nsig");
if(i<50) sum50+=sData->GetSWeight(i,"nsig");
if(i<100) sum100+=sData->GetSWeight(i,"nsig");
if(i<200) sum200+=sData->GetSWeight(i,"nsig");
if(i<300) sum300+=sData->GetSWeight(i,"nsig");
if(i<600) sum600+=sData->GetSWeight(i,"nsig");
if(i<900) sum900+=sData->GetSWeight(i,"nsig");
if(i<1200) sum1200+=sData->GetSWeight(i,"nsig");
}
myfile.close();
std::cout << std::endl;
std::cout<<"Sum of the sWeights is: "<<total<<std::endl;
std::cout<<"Sum of the first 20 sWeights is: "<<sum20<<std::endl;
std::cout<<"Sum of the first 50 sWeights is: "<<sum50<<std::endl;
std::cout<<"Sum of the first 100 sWeights is: "<<sum100<<std::endl;
std::cout<<"Sum of the first 200 sWeights is: "<<sum200<<std::endl;
std::cout<<"Sum of the first 300 sWeights is: "<<sum300<<std::endl;
std::cout<<"Sum of the first 600 sWeights is: "<<sum600<<std::endl;
std::cout<<"Sum of the first 900 sWeights is: "<<sum900<<std::endl;
std::cout<<"Sum of the first 1200 sWeights is: "<<sum1200<<std::endl;
std::cout<<"Total # of events: "<<data->numEntries()<<std::endl;
// import this new dataset with sWeights
std::cout << "import new dataset with sWeights" << std::endl;
ws->import(*data, Rename("dataWithSWeights"));
}
