readExclusion() {
TFile * file = new TFile("theExclusion_M115_EGMLOOSE006.root");
HypoTestInverterResult * r = dynamic_cast<HypoTestInverterResult*>( file->Get("result_r") );
double upperLimit = r->UpperLimit();
double ulError = r->UpperLimitEstimatedError();
std::cout << "The computed upper limit is: " << upperLimit << " +/- " << ulError << std::endl;
const int nEntries = r->ArraySize();
TCanvas *c0 = new TCanvas("c0","coucou",600,600);
c0->SetFillColor(0);
TString plotTitle = "CL Scan for workspace";
HypoTestInverterPlot *plot = new HypoTestInverterPlot("HTI_Result_Plot",plotTitle,r);
plot->Draw("CLb 2CL"); // plot all and Clb
c0->Print("limit_plot.gif");
const int nEntries = r->ArraySize();
cout << "N entries = " << nEntries << endl;
TCanvas * c2 = new TCanvas();
c2->Divide( 2, TMath::Ceil(nEntries/2));
for (int i=0; i<nEntries; i++) {
c2->cd(i+1);
SamplingDistPlot * pl = plot->MakeTestStatPlot(i);
pl->SetLogYaxis(true);
pl->Draw();
}
}
void central_interval_Hybrid_fixed_scan(Model* model,double confidence,int n_toys_per_point,double lower_scan_bound, double upper_scan_bound,int n_steps,int random_seed=0,bool useCLS=true){
cout<<"///////////////////////////////////////////////////////////////////////////////////////////"<<endl;
cout<<"Calculating central interval with the Hybrid method"<<endl;
cout<<"///////////////////////////////////////////////////////////////////////////////////////////"<<endl;
//set the random seed
RooRandom::randomGenerator()->SetSeed(random_seed);
//get the calculator
HybridCalculatorOriginal myhc(*model->get_data(),*model->get_sb_likelihood(),*model->get_b_likelihood());
//for numbercounting experiments
myhc.PatchSetExtended(false);
//set likelihood ratio as the test statistics
myhc.SetTestStatistic(1);
//define the systematics to be used
if (model->get_nuisance_set()) {
myhc.UseNuisance(true);
myhc.SetNuisancePdf(*model->get_nuisance_prior_pdf());
myhc.SetNuisanceParameters(*model->get_nuisance_set());
} else {
myhc.UseNuisance(false);
}
//define the number of toys to be done
myhc.SetNumberOfToys(n_toys_per_point);
//get the interval calculator
HypoTestInverter myInverter(myhc,*model->get_POI());
//use the CLS method
myInverter.UseCLs(useCLS);
//set the confidence
myInverter.SetTestSize(1-confidence);
//run the auto fixed in range lower_scan_bound - upper_scan_bound with n_steps steps
myInverter.RunFixedScan(n_steps,lower_scan_bound,upper_scan_bound);
//get the result
HypoTestInverterResult* results = myInverter.GetInterval();
double upperLimit = results->UpperLimit();
double lowerLimit = results->LowerLimit();
std::cout <<confidence<<"% interval is: " << lowerLimit<<" , "<< upperLimit << std::endl;
}
void rs801_HypoTestInverterOriginal()
{
// prepare the model
RooRealVar lumi("lumi","luminosity",1);
RooRealVar r("r","cross-section ratio",3.74,0,50);
RooFormulaVar ns("ns","1*r*lumi",RooArgList(lumi,r));
RooRealVar nb("nb","background yield",1);
RooRealVar x("x","dummy observable",0,1);
RooConstVar p0(RooFit::RooConst(0));
RooPolynomial flatPdf("flatPdf","flat PDF",x,p0);
RooAddPdf totPdf("totPdf","S+B model",RooArgList(flatPdf,flatPdf),RooArgList(ns,nb));
RooExtendPdf bkgPdf("bkgPdf","B-only model",flatPdf,nb);
RooDataSet* data = totPdf.generate(x,1);
// prepare the calculator
HybridCalculatorOriginal myhc(*data, totPdf, bkgPdf,0,0);
myhc.SetTestStatistic(2);
myhc.SetNumberOfToys(1000);
myhc.UseNuisance(false);
// run the hypothesis-test invertion
HypoTestInverterOriginal myInverter(myhc,r);
myInverter.SetTestSize(0.10);
myInverter.UseCLs(true);
// myInverter.RunFixedScan(5,1,6);
// scan for a 95% UL
myInverter.RunAutoScan(3.,5,myInverter.Size()/2,0.005);
// run an alternative autoscan algorithm
// myInverter.RunAutoScan(1,6,myInverter.Size()/2,0.005,1);
//myInverter.RunOnePoint(3.9);
HypoTestInverterResult* results = myInverter.GetInterval();
HypoTestInverterPlot myInverterPlot("myInverterPlot","",results);
TGraphErrors* gr1 = myInverterPlot.MakePlot();
gr1->Draw("ALP");
double ulError = results->UpperLimitEstimatedError();
double upperLimit = results->UpperLimit();
std::cout << "The computed upper limit is: " << upperLimit << std::endl;
std::cout << "an estimated error on this upper limit is: " << ulError << std::endl;
// expected result: 4.10
}
double countingExperiment95CLUpperLimit (int nObserved, double bkgMean, double bkgSigma, int nToys=500) {
// expect nothing beyond 5 sigma
double bkgMaxExpected = bkgMean+5*bkgSigma; // 5 sigma
double signalMaxExpected = nObserved+5*sqrt(double(nObserved))-bkgMean+5*bkgSigma;
// variables
RooRealVar signal ("signal", "signal", 0, 0, signalMaxExpected);
RooRealVar bkg ("bkg", "bkg", bkgMean, 0, bkgMaxExpected);
RooRealVar flatUnit ("flatUnit","flatUnit",0.,1.);
// sets
RooArgSet nuisPar (bkg);
RooArgSet poi (signal);
// constants
RooRealVar bkgMeanVar ("bkgMean", "bkgMean", bkgMean);
RooRealVar bkgSigmaVar ("bkgSigma", "bkgSigma", bkgSigma);
// PDFs
RooUniform signalPdf ("signalPdf","signalPdf",flatUnit);
RooUniform backgroundPdf ("backgroundPdf","backgroundPdf",flatUnit);
RooAddPdf modelPdf ("model","model",RooArgList(signalPdf,backgroundPdf),RooArgList(signal,bkg));
RooExtendPdf modelBkgPdf ("modelBkg","modelBkg",backgroundPdf,bkg);
RooGaussian priorBkgPdf ("priorBkg","priorBkg",bkg,bkgMeanVar,bkgSigmaVar);
RooDataSet* data = modelPdf.generate(flatUnit, nObserved);
HybridCalculator hc(*data, modelPdf, modelBkgPdf, &nuisPar, &priorBkgPdf);
hc.SetTestStatistic(2); // # of events
hc.SetNumberOfToys(nToys);
hc.UseNuisance(true);
HypoTestInverter myInverter(hc,signal);
double targetP = 0.05; // 95% C.L.
myInverter.RunAutoScan(0, signalMaxExpected, targetP, 0.1*targetP);
HypoTestInverterResult* results = myInverter.GetInterval();
results->SetConfidenceLevel (1-2.*targetP);
double upperLimit = results->UpperLimit();
delete data;
// delete results;
return upperLimit;
}
readExclusion_M130(){
TString massPointString = IntToString(massPoint);
TFile * file = new TFile("theExclusion_M"+massPointString+"_"+nomPlot+".root");
HypoTestInverterResult * r = dynamic_cast<HypoTestInverterResult*>( file->Get("result_r") );
double upperLimit = r->UpperLimit();
double ulError = r->UpperLimitEstimatedError();
std::cout << "The computed upper limit is: " << upperLimit << " +/- " << ulError << std::endl;
const int nEntries = r->ArraySize();
TCanvas *c0 = new TCanvas("c0","coucou",600,600);
c0->SetFillColor(0);
TString plotTitle = "CL Scan for workspace";
HypoTestInverterPlot *plot = new HypoTestInverterPlot("HTI_Result_Plot",plotTitle,r);
plot->Draw("CLb 2CL");//("CLb 2CL"); // plot all and Clb
c0->Print("limit_plot_M"+massPointString+".gif");
const int nEntries = r->ArraySize();
cout << "N entries = " << nEntries << endl;
TCanvas * c2 = new TCanvas();
c2->Divide( 2, TMath::Ceil(nEntries/2));
for (int i=0; i<nEntries; i++) {
c2->cd(i+1);
SamplingDistPlot * pl = plot->MakeTestStatPlot(i);
pl->SetLogYaxis(true);
pl->Draw();
}
std::cout << "The computed upper limit is: " << upperLimit << " +/- " << ulError << std::endl;
std::cout << "The expected limi is : " << r->GetExpectedUpperLimit(0) << endl;
float expected = r->GetExpectedUpperLimit(0);
float observed = r->UpperLimit();
float observedError = r->UpperLimitEstimatedError();
float expected1sP = r->GetExpectedUpperLimit(1);
float expected2sP = r->GetExpectedUpperLimit(2);
float expected1sM = r->GetExpectedUpperLimit(-1);
float expected2sM = r->GetExpectedUpperLimit(-2);
cout << "ZZZZ graph->SetPoint(" <<number << "," << massPoint<< "," << expected << ");" << endl;
cout << "ZZZZ grae->SetPoint(" <<number << "," << massPoint<< "," << expected << ");" << endl;
cout << "ZZZZ grae->SetPointError(" << number << ",0,0," << (expected-expected1sM) << "," << (expected1sP-expected) << ");" << endl;
cout << "ZZZZ grae2->SetPoint(" <<number << "," << massPoint<< "," << expected << ");" << endl;
cout << "ZZZZ grae2->SetPointError(" << number << ",0,0," << (expected-expected2sM) << "," << (expected2sP-expected) << ");" << endl;
cout << "ZZZZ gre->SetPoint(" <<number << "," << massPoint<< "," << observed << ");" << endl;
cout << "ZZZZ gre->SetPointError(" <<number << ",0," << observedError <<");" << endl;
}
void RA2bHypoTestInvDemo(const char * fileName =0,
const char * wsName = "combined",
const char * modelSBName = "ModelConfig",
const char * modelBName = "",
const char * dataName = "obsData",
int calculatorType = 0,
int testStatType = 3,
bool useCls = true ,
int npoints = 5,
double poimin = 0,
double poimax = 5,
int ntoys=1000,
int mgl = -1,
int mlsp = -1,
const char * outFileName = "test")
{
/*
Other Parameter to pass in tutorial
apart from standard for filename, ws, modelconfig and data
type = 0 Freq calculator
type = 1 Hybrid
testStatType = 0 LEP
= 1 Tevatron
= 2 Profile Likelihood
= 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat)
useCLs scan for CLs (otherwise for CLs+b)
npoints: number of points to scan , for autoscan set npoints = -1
poimin,poimax: min/max value to scan in case of fixed scans
(if min >= max, try to find automatically)
ntoys: number of toys to use
extra options are available as global paramters of the macro. They are:
plotHypoTestResult plot result of tests at each point (TS distributions)
useProof = true;
writeResult = true;
nworkers = 4;
*/
if (fileName==0) {
fileName = "results/example_combined_GaussExample_model.root";
std::cout << "Use standard file generated with HistFactory :" << fileName << std::endl;
}
TFile * file = new TFile(fileName);
RooWorkspace * w = dynamic_cast<RooWorkspace*>( file->Get(wsName) );
HypoTestInverterResult * r = 0;
std::cout << w << "\t" << fileName << std::endl;
if (w != NULL) {
r = RunInverter(w, modelSBName, modelBName, dataName, calculatorType, testStatType, npoints, poimin, poimax, ntoys, useCls );
if (!r) {
std::cerr << "Error running the HypoTestInverter - Exit " << std::endl;
return;
}
}
else
{
// case workspace is not present look for the inverter result
std::cout << "Reading an HypoTestInverterResult with name " << wsName << " from file " << fileName << std::endl;
r = dynamic_cast<HypoTestInverterResult*>( file->Get(wsName) ); //
if (!r) {
std::cerr << "File " << fileName << " does not contain a workspace or an HypoTestInverterResult - Exit "
<< std::endl;
file->ls();
return;
}
}
printf("\n\n") ;
HypoTestResult* htr = r->GetResult(0) ;
printf(" Data value for test stat : %7.3f\n", htr->GetTestStatisticData() ) ;
printf(" CLsplusb : %9.4f\n", r->CLsplusb(0) ) ;
printf(" CLb : %9.4f\n", r->CLb(0) ) ;
printf(" CLs : %9.4f\n", r->CLs(0) ) ;
printf("\n\n") ;
cout << flush ;
double upperLimit = r->UpperLimit();
double ulError = r->UpperLimitEstimatedError();
std::cout << "The computed upper limit is: " << upperLimit << " +/- " << ulError << std::endl;
const int nEntries = r->ArraySize();
const char * typeName = (calculatorType == 0) ? "Frequentist" : "Hybrid";
const char * resultName = (w) ? w->GetName() : r->GetName();
TString plotTitle = TString::Format("%s CL Scan for workspace %s",typeName,resultName);
HypoTestInverterPlot *plot = new HypoTestInverterPlot("HTI_Result_Plot",plotTitle,r);
TCanvas* c1 = new TCanvas() ;
plot->Draw("CLb 2CL"); // plot all and Clb
c1->Update() ;
c1->SaveAs("cls-canv1.png") ;
c1->SaveAs("cls-canv1.pdf") ;
if (plotHypoTestResult) {
TCanvas * c2 = new TCanvas();
c2->Divide( 2, TMath::Ceil(nEntries/2));
for (int i=0; i<nEntries; i++) {
c2->cd(i+1);
SamplingDistPlot * pl = plot->MakeTestStatPlot(i);
pl->SetLogYaxis(true);
pl->Draw();
}
c2->Update() ;
c2->SaveAs("cls-canv2.png") ;
c2->SaveAs("cls-canv2.pdf") ;
}
std::cout << " expected limit (median) " << r->GetExpectedUpperLimit(0) << std::endl;
std::cout << " expected limit (-1 sig) " << r->GetExpectedUpperLimit(-1) << std::endl;
std::cout << " expected limit (+1 sig) " << r->GetExpectedUpperLimit(1) << std::endl;
// save 2d histograms bin to file
TH2F *result = new TH2F("result","result",22,100,1200,23,50,1200);
TH2F *exp_res = new TH2F("exp_res","exp_res",22,100,1200,23,50,1200);
TH2F *exp_res_minus = new TH2F("exp_res_minus","exp_res_minus",22,100,1200,23,50,1200);
TH2F *exp_res_plus = new TH2F("exp_res_plus","exp_res_plus",22,100,1200,23,50,1200);
result->Fill(mgl,mlsp,upperLimit);
exp_res->Fill(mgl,mlsp,r->GetExpectedUpperLimit(0));
exp_res_minus->Fill(mgl,mlsp,r->GetExpectedUpperLimit(-1));
exp_res_plus->Fill(mgl,mlsp,r->GetExpectedUpperLimit(1));
TFile *f = new TFile(outFileName,"RECREATE");
f->cd();
result->Write();
exp_res->Write();
exp_res_minus->Write();
exp_res_plus->Write();
f->Close();
if (w != NULL && writeResult) {
// write to a file the results
const char * calcType = (calculatorType == 0) ? "Freq" : "Hybr";
const char * limitType = (useCls) ? "CLs" : "Cls+b";
const char * scanType = (npoints < 0) ? "auto" : "grid";
TString resultFileName = TString::Format("%s_%s_%s_ts%d_",calcType,limitType,scanType,testStatType);
resultFileName += fileName;
TFile * fileOut = new TFile(resultFileName,"RECREATE");
r->Write();
fileOut->Close();
}
}
void test_counting_experiment() {
////////////////////// MODEL BUILDING /////////////////////////////////
///////////////////////////////////////////////////////////////////////////
/*
N_s = N_tot_theory(Mass,Xsec) * Acceptance_SR * Eff_AmBe_bin_i * mu
N_b = N_Co_SR_bin_i * Norm_factor
Xesec: considered 10^-40 cm^2
Norm_factor = N_Data_CR / N_Co_CR --> assuming no difference between Co and Data in CR and SR.
N_tot_theory(Mass,Xsec): for 225 livedays, 45kg and considering Xsec. It is a constant, no uncertainty at the moment.
---Costraint Signal
nuissance parameter = Acceptance_SR, Eff_AmBe_bin_i
Gauss(Acceptance_SR_obs | Acceptance_SR, err.)
Poisson(S0_i | S_tot_SR * Eff_AmBe_bin_i)
---Costraint Bkg
nuissance parameter = N_Co_SR_bin_i, Norm_factor
Gauss(Norm_factor_obs | Norm_factor, err)
Poisson(B0_i | N_Co_SR_bin_i)
---- WARNING:: convergence problems: mu_hat should always be >> 1, too small values have problem in finding minimum
because mu is set >0. ---> Try to fix Xsec in order to have mu_hat ~ 10
*/
RooWorkspace w("w");
//gROOT->ProcessLine(".L retrieve_input_from_histo_NoSys.C+");
gROOT->ProcessLine(".L retrieve_input_from_histo.C+");
retrieve_input_from_histo(w);
// Building the model
ModelConfig mc("ModelConfig",&w);
mc.SetPdf(*w.pdf("model"));
mc.SetParametersOfInterest(*w.var("mu"));
// Setting nuissance parameter
mc.SetNuisanceParameters(*w.set("nuissance_parameter"));
// need now to set the global observable
mc.SetGlobalObservables(*w.set("g_observables"));
mc.SetObservables(*w.set("observables"));
// this is needed for the hypothesis tests
mc.SetSnapshot(*w.var("mu"));
// make data set with the number of observed events
RooDataSet data("data","", *w.set("observables"));
data.add(*w.set("observables"));
// import data set in workspace and save it in a file
w.import(data);
// import model in the workspace
w.import(mc);
w.writeToFile("CountingModel.root", true);
w.Print();
data.Print();
/*
cout << w.var("S_i")->getValV() << endl;//<< " " << w.var("S_i_exp")->getValV() << endl;
///////////////////////////////////////////////////////////////////////
ProfileLikelihoodCalculator pl(data,mc);
pl.SetConfidenceLevel(0.95);
LikelihoodInterval* interval = pl.GetInterval();
// find the iterval on the first Parameter of Interest
RooRealVar* firstPOI = (RooRealVar*) mc.GetParametersOfInterest()->first();
double lowerLimit = interval->LowerLimit(*firstPOI);
double upperLimit = interval->UpperLimit(*firstPOI);
cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
lowerLimit << ", "<<
upperLimit <<"] "<<endl;
LikelihoodIntervalPlot * plot = new LikelihoodIntervalPlot(interval);
// plot->SetRange(0,50); // possible eventually to change ranges
//plot->SetNPoints(50); // do not use too many points, it could become very slow for some models
plot->Draw(""); // use option TF1 if too slow (plot.Draw("tf1")
*/
////////////////////////// hypo test
// get the modelConfig (S+B) out of the file
// and create the B model from the S+B model
ModelConfig * sbModel = (ModelConfig*) mc.Clone();
sbModel->SetName("S+B Model");
RooRealVar* poi = (RooRealVar*) sbModel->GetParametersOfInterest()->first();
poi->setVal(1); // set POI snapshot in S+B model for expected significance
sbModel->SetSnapshot(*poi);
ModelConfig * bModel = (ModelConfig*) mc.Clone();
bModel->SetName("B Model");
RooRealVar* poi2 = (RooRealVar*) bModel->GetParametersOfInterest()->first();
poi2->setVal(0);
bModel->SetSnapshot( *poi2 );
//------------------Limit calculation for N_th event expected = 10
AsymptoticCalculator ac(data, *bModel, *sbModel);
//ac.SetOneSidedDiscovery(true); // for one-side discovery test
// ac.SetOneSided(true); // for one-side tests (limits)
ac.SetQTilde(true);
ac.SetPrintLevel(2); // to suppress print level
// create hypotest inverter
// passing the desired calculator
HypoTestInverter *calc = new HypoTestInverter(ac); // for asymptotic
//HypoTestInverter calc(fc); // for frequentist
calc->SetConfidenceLevel(0.90);
//calc->UseCLs(false);
calc->UseCLs(true);
int npoints = 500; // number of points to scan
//int npoints = 1000; // number of points to scan default 1000
// min and max (better to choose smaller intervals)
double poimin = poi->getMin();
double poimax = poi->getMax();
//poimin = 0; poimax=10;
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc->SetFixedScan(npoints,poimin,poimax);
calc->SetVerbose(2);
HypoTestInverterResult * r = calc->GetInterval();
double upperLimit = r->UpperLimit();
std::cout << "The computed Expected upper limit is: " << r->GetExpectedUpperLimit(0) << std::endl;
//------------ Getting the interval as function of m --------------//
/* ifstream in;
in.open("integral_mass.dat");
vector <double> masses_v;
vector <double> observed_v;
vector <double> expected_v;
vector <double> expected_gaud_v;
vector <double> expected_S1_up_v;
vector <double> expected_S1_dw_v;
vector <double> expected_S2_up_v;
vector <double> expected_S2_dw_v;
double mass_itr =0.;
double Nev_exp_th_itr =0.;
double xsec_modifier = 10.;
double N_tot_theory = w.var("N_tot_theory")->getValV();
while(mass_itr <1000.){
in >> mass_itr;
in >> Nev_exp_th_itr;
xsec_modifier = Nev_exp_th_itr * 225.009 * 34.; //225.009 livedays and 34 kg and 10^-40 cm2 Xsec.
masses_v.push_back(mass_itr);
observed_v.push_back( 1.e-40 * N_tot_theory / xsec_modifier * upperLimit );
expected_v.push_back( 1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(0) );
expected_gaud_v.push_back(7e-38 * 1.37590955945e-05 / Nev_exp_th_itr );
expected_S1_up_v.push_back(1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(1));
expected_S2_up_v.push_back(1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(2));
expected_S2_dw_v.push_back(1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(-2));
expected_S1_dw_v.push_back(1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(-1));
cout << "Expected median limit for mass " << mass_itr << " GeV = " << 1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(0) << " cm^2 " << endl;
// observed_v.push_back( w.var("Xsec")->getValV() * w.var("K_m")->getValV()* upperLimit );
// expected_v.push_back( w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(0) );
// expected_S1_up_v.push_back(w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(1));
// expected_S2_up_v.push_back(w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(2));
// expected_S2_dw_v.push_back(w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(-2));
// expected_S1_dw_v.push_back(w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(-1));
}
in.close();
const int n = masses_v.size();
double xe[n];
double mA[n];
double observed[n];
double expected[n];
double expected_gaudenz[n];
double exSigma1_l[n];
double exSigma1_u[n];
double exSigma2_l[n];
double exSigma2_u[n];
for(int k=0; k< n; k++){
mA[k] = masses_v[k];
observed[k] = observed_v[k];
expected[k] = expected_v[k];
expected_gaudenz[k] = expected_gaud_v[k];
exSigma1_l[k] =expected_v[k] - expected_S1_dw_v[k] ;
exSigma1_u[k] = expected_S1_up_v[k] - expected_v[k];
exSigma2_l[k] = expected_v[k] - expected_S2_dw_v[k];
exSigma2_u[k] = expected_S2_up_v[k] - expected_v[k] ;
}
TGraphErrors *obs_limits = new TGraphErrors(n, mA, observed);
TGraphErrors *Exp_limits = new TGraphErrors(n, mA, expected );
TGraphAsymmErrors *Exp_limitsS1 = new TGraphAsymmErrors(n, mA, expected ,xe, xe, exSigma1_l, exSigma1_u );
TGraphAsymmErrors *Exp_limitsS2 = new TGraphAsymmErrors(n, mA, expected ,xe, xe, exSigma2_l, exSigma2_u);
TGraphErrors *Exp_limits_gaudenz = new TGraphErrors( n, mA, expected_gaudenz);
//double expected_xmass[15] = {8e-36,7e-37, 2e-37, 1e-37, 8e-38, 6e-38, 5.5e-38, 5e-38, 4.3e-38, 5e-38, 6e-38, 7e-38, 9e-38, 1.2e-37, 1.5e-37};
//double m_xmass[15] = { 20, 30., 40., 50., 60., 70., 80., 90., 100., 200., 300., 400, 500.,700., 1000.};
TGraphErrors *Exp_limits_xmass = new TGraphErrors(16);
Exp_limits_xmass->SetPoint(0,20,8e-36);
Exp_limits_xmass->SetPointError(0,0,0);
Exp_limits_xmass->SetPoint(1,29.8071,7.162923e-37);
Exp_limits_xmass->SetPointError(1,0,0);
Exp_limits_xmass->SetPoint(2,39.90202,2.027528e-37);
Exp_limits_xmass->SetPointError(2,0,0);
Exp_limits_xmass->SetPoint(3,53.41583,9.91722e-38);
Exp_limits_xmass->SetPointError(3,0,0);
Exp_limits_xmass->SetPoint(4,62.16429,7.461589e-38);
Exp_limits_xmass->SetPointError(4,0,0);
Exp_limits_xmass->SetPoint(5,69.85718,6.3506e-38);
Exp_limits_xmass->SetPointError(5,0,0);
Exp_limits_xmass->SetPoint(6,83.21777,5.354015e-38);
Exp_limits_xmass->SetPointError(6,0,0);
Exp_limits_xmass->SetPoint(7,90,5e-38);
Exp_limits_xmass->SetPointError(7,0,0);
Exp_limits_xmass->SetPoint(8,105.0887,4.600252e-38);
Exp_limits_xmass->SetPointError(8,0,0);
Exp_limits_xmass->SetPoint(9,200,5e-38);
Exp_limits_xmass->SetPointError(9,0,0);
Exp_limits_xmass->SetPoint(10,300,6e-38);
Exp_limits_xmass->SetPointError(10,0,0);
Exp_limits_xmass->SetPoint(11,388.2045,7.252295e-38);
Exp_limits_xmass->SetPointError(11,0,0);
Exp_limits_xmass->SetPoint(12,590.8438,9.823615e-38);
Exp_limits_xmass->SetPointError(12,0,0);
Exp_limits_xmass->SetPoint(13,746.1269,1.210266e-37);
Exp_limits_xmass->SetPointError(13,0,0);
Exp_limits_xmass->SetPoint(14,1000,1.5e-37);
Exp_limits_xmass->SetPointError(14,0,0);
Exp_limits_xmass->SetPoint(15,4244.204,4.354065e-37);
Exp_limits_xmass->SetPointError(15,0,0);
TCanvas *c1 = new TCanvas("limits", "limit", 600, 600);
Exp_limitsS1->SetFillColor(3);
Exp_limitsS1->SetLineColor(3);
Exp_limitsS1->SetMarkerColor(3);
Exp_limitsS1->SetMarkerSize(0);
Exp_limitsS2->SetFillColor(5);
Exp_limitsS2->SetLineColor(5);
Exp_limitsS2->SetMarkerColor(5);
Exp_limitsS2->SetMarkerSize(0);
obs_limits->SetFillColor(0);
obs_limits->SetLineWidth(3);
obs_limits->SetMarkerSize(0);
Exp_limits->SetFillColor(0);
Exp_limits->SetMarkerSize(0);
Exp_limits->SetLineStyle(7);
Exp_limits->SetLineWidth(3);
Exp_limits_gaudenz->SetFillColor(0);
Exp_limits_gaudenz->SetMarkerSize(0);
Exp_limits_gaudenz->SetLineWidth(3);
Exp_limits_gaudenz->SetLineColor(4);
Exp_limits_xmass->SetFillColor(0);
Exp_limits_xmass->SetMarkerSize(0);
Exp_limits_xmass->SetLineWidth(3);
Exp_limits_xmass->SetLineColor(2);
//Exp_limitsS2->GetYaxis()->SetTitle("#sigma#timesBR( #phi #rightarrow #tau#tau ) [pb]");
Exp_limitsS2->GetYaxis()->SetTitle("#sigma");
Exp_limitsS2->GetXaxis()->SetTitle("M [GeV]");
Exp_limitsS2->GetXaxis()->SetLimits(9.,1000.);
Exp_limitsS2->GetYaxis()->SetRangeUser(1E-38,1E-30);
Exp_limits->GetXaxis()->SetLimits(9.,1000.);
Exp_limits->GetYaxis()->SetRangeUser(1E-38,1E-30);
Exp_limitsS2->Draw("Al3");
Exp_limitsS1->Draw("sameL3");
Exp_limits->Draw("PL");
Exp_limits_gaudenz->Draw("PC");
Exp_limits_xmass->Draw("PC");
//obs_limits->Draw("PL");
TLegend* lego = new TLegend(0.2,0.9,0.5,0.7);
lego->SetTextSize(0.033);
lego->SetFillColor(0);
lego->SetBorderSize(0);
lego->AddEntry(obs_limits,"Observed 90\% CLs limit");
lego->AddEntry(Exp_limits_gaudenz, "Expected 90\% Gaudenz");
lego->AddEntry(Exp_limits_xmass, "Expected 90\% XMASS");
lego->AddEntry(Exp_limits, "Expected 90\% CLs limit");
lego->AddEntry(Exp_limitsS1,"1 #sigma","f");
lego->AddEntry(Exp_limitsS2,"2 #sigma","f");
lego->Draw();
gPad->SetLogy();
gPad->SetLogx();
gPad->RedrawAxis("g");
myText(0.4,0.86,2,"Test");
*/
// now use the profile inspector
ProfileInspector p;
TList* list = p.GetListOfProfilePlots(data,&mc);
// now make plots
TCanvas* c1 = new TCanvas("c1","ProfileInspectorDemo",800,200);
if(list->GetSize()>4){
double n = list->GetSize();
int nx = (int)sqrt(n) ;
int ny = TMath::CeilNint(n/nx);
nx = TMath::CeilNint( sqrt(n) );
c1->Divide(ny,nx);
} else
c1->Divide(list->GetSize());
for(int i=0; i<list->GetSize(); ++i){
c1->cd(i+1);
list->At(i)->Draw("al");
}
cout << endl;
/* // plot now the result of the scan
HypoTestInverterPlot *plot = new HypoTestInverterPlot("HTI_Result_Plot","HypoTest Scan Result",r);
// plot in a new canvas with style
TCanvas * c1 = new TCanvas("HypoTestInverter Scan");
c1->SetLogy(false);
plot->Draw("2CL"); // plot also CLb and CLs+b
//plot->Draw("OBS"); // plot only observed p-value
*/
// plot also in a new canvas the test statistics distributions
// plot test statistics distributions for the two hypothesis
/* // when distribution is generated (case of FrequentistCalculators)
const int n = r->ArraySize();
if (n> 0 && r->GetResult(0)->GetNullDistribution() ) {
TCanvas * c2 = new TCanvas("Test Statistic Distributions","",2);
if (n > 1) {
int ny = TMath::CeilNint( sqrt(n) );
int nx = TMath::CeilNint(double(n)/ny);
c2->Divide( nx,ny);
}
for (int i=0; i<n; i++) {
if (n > 1) c2->cd(i+1);
SamplingDistPlot * pl = plot->MakeTestStatPlot(i);
pl->SetLogYaxis(true);
pl->Draw();
}
}
*/
}
void HypoTestInvDemo(const char * fileName ="GausModel_b.root",
const char * wsName = "w",
const char * modelSBName = "model_sb",
const char * modelBName = "model_b",
const char * dataName = "data_obs",
int type = 0, // calculator type
int testStatType = 0, // test stat type
int npoints = 10,
int ntoys=1000,
bool useCls = true )
{
/*
type = 0 Freq calculator
type = 1 Hybrid
testStatType = 0 LEP
= 1 Tevatron
= 2 PL
*/
if (fileName==0) {
std::cout << "give input filename " << std::endl;
return;
}
TFile * file = new TFile(fileName);
RooWorkspace * w = dynamic_cast<RooWorkspace*>( file->Get(wsName) );
if (!w) {
return;
}
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("HypoTestDemo","Not existing data %s",dataName);
}
// get models from WS
// get the modelConfig out of the file
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
SimpleLikelihoodRatioTestStat slrts(*bModel->GetPdf(),*sbModel->GetPdf());
slrts.SetNullParameters(*bModel->GetSnapshot());
slrts.SetAltParameters(*sbModel->GetSnapshot());
RatioOfProfiledLikelihoodsTestStat
ropl(*bModel->GetPdf(), *sbModel->GetPdf(), sbModel->GetSnapshot());
ropl.SetSubtractMLE(false);
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
profll.SetOneSided(0);
TestStatistic * testStat = &slrts;
if (testStatType == 1) testStat = &ropl;
if (testStatType == 2) testStat = &profll;
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *sbModel, *bModel);
else new HybridCalculator(*data, *sbModel, *bModel);
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
//toymcs->SetNEventsPerToy(1);
toymcs->SetTestStatistic(testStat);
if (type == 1) {
HybridCalculator *hhc = (HybridCalculator*) hc;
hhc->SetToys(ntoys,ntoys);
// hhc->ForcePriorNuisanceAlt(*pdfNuis);
// hhc->ForcePriorNuisanceNull(*pdfNuis);
}
else
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys);
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
TStopwatch tw; tw.Start();
const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
RooRealVar *poi = (RooRealVar*)poiSet->first();
// fit the data first
sbModel->GetPdf()->fitTo(*data);
double poihat = poi->getVal();
//poi->setVal(30);
//poi->setError(10);
HypoTestInverter calc(*hc);
// GENA: for two-sided interval
//calc.SetConfidenceLevel(0.95);
// GENA: for 95% upper limit
calc.SetConfidenceLevel(0.90);
calc.UseCLs(useCls);
calc.SetVerbose(true);
// can spped up using proof
ProofConfig pc(*w, 2, "workers=2", kFALSE);
//ProofConfig pc(*w, 30, "localhost", kFALSE);
//ToyMCSampler * toymcs = dynamic_cast<ToyMCSampler *> (calc.GetHypoTestCalculator()->GetTestStatSampler() );
// GENA: disable proof for now
//toymcs->SetProofConfig(&pc); // enable proof
if (npoints > 0) {
// GENA
double poimin = TMath::Max(poihat - 4 * poi->getError(), 0.0);
//poimin = poihat;
double poimax = poihat + 4 * poi->getError();
poimin = 0;
poimax = 20;
//double poimin = poi->getMin();
//double poimax = poi->getMax();
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc.SetFixedScan(npoints,poimin,poimax);
}
HypoTestInverterResult * r = calc.GetInterval();
// write to a file the results
TString resultFileName = (useCls) ? "CLs_" : "Cls+b_";
resultFileName += fileName;
// GENA
//TFile * file = new TFile(resultFileName,"RECREATE");
file = new TFile(resultFileName,"RECREATE");
r->Write();
file->Close();
double ulError = r->UpperLimitEstimatedError();
double upperLimit = r->UpperLimit();
std::cout << "The computed upper limit is: " << upperLimit << std::endl;
std::cout << "an estimated error on this upper limit is: " << ulError << std::endl;
// check using interpolation
// double interpLimit = r->FindInterpolatedLimit(1.-r->ConfidenceLevel() );
// cout << "The computer interpolated limits is " << interpLimit << endl;
const int nEntries = r->ArraySize();
std::vector<Double_t> xArray(nEntries);
std::vector<Double_t> yArray(nEntries);
std::vector<Double_t> yErrArray(nEntries);
for (int i=0; i<nEntries; i++) {
xArray[i] = r->GetXValue(i);
yArray[i] = r->GetYValue(i);
yErrArray[i] = r->GetYError(i);
std::cout << xArray[i] << " , " << yArray[i] << " err = " << yErrArray[i] << std::endl;
}
// see expected result (bands)
TGraph * g0 = new TGraph(nEntries);
TGraphAsymmErrors * g1 = new TGraphAsymmErrors(nEntries);
TGraphAsymmErrors * g2l = new TGraphAsymmErrors(nEntries);
TGraphAsymmErrors * g2u = new TGraphAsymmErrors(nEntries);
double p[7];
double q[7];
p[0] = ROOT::Math::normal_cdf(-2);
p[1] = ROOT::Math::normal_cdf(-1.5);
p[2] = ROOT::Math::normal_cdf(-1);
p[3] = 0.5;
p[4] = ROOT::Math::normal_cdf(1);
p[5] = ROOT::Math::normal_cdf(1.5);
p[6] = ROOT::Math::normal_cdf(2);
for (int i=0; i<nEntries; i++) {
SamplingDistribution * s = r->GetExpectedDistribution(i);
// GENA
//const std::vector<double> & values = s->GetSamplingDistribution();
const std::vector<Double_t> & cValues = s->GetSamplingDistribution();
std::vector<Double_t> values;
for (std::vector<Double_t>::const_iterator val = cValues.begin();
val != cValues.end();
++val) values.push_back(*val);
TMath::Quantiles(values.size(), 7, &values[0],q,p,false);
double p0 = q[3];
double p2l = q[1];
double p2u = q[5];
g0->SetPoint(i, r->GetXValue(i), p0 ) ;
g1->SetPoint(i, r->GetXValue(i), p0);
g2l->SetPoint(i, r->GetXValue(i), p2l);
g2u->SetPoint(i, r->GetXValue(i), p2u);
//g2->SetPoint(i, r->GetXValue(i), s->InverseCDF(0.50));
g1->SetPointEYlow(i, q[3] - q[2]); // -1 sigma errorr
g1->SetPointEYhigh(i, q[4] - q[3]);//+1 sigma error
g2l->SetPointEYlow(i, q[1]-q[0]); // -2 -- -1 sigma error
g2l->SetPointEYhigh(i, q[2]-q[1]);
g2u->SetPointEYlow(i, q[5]-q[4]);
g2u->SetPointEYhigh(i, q[6]-q[5]);
if (plotHypoTestResult) {
HypoTestResult * hr = new HypoTestResult();
hr->SetNullDistribution( r->GetBackgroundDistribution() );
hr->SetAltDistribution( r->GetSignalAndBackgroundDistribution(i) );
new TCanvas();
HypoTestPlot * pl = new HypoTestPlot(*hr);
pl->Draw();
}
}
HypoTestInverterPlot *plot = new HypoTestInverterPlot("result","",r);
TGraphErrors * g = plot->MakePlot();
g->Draw("APL");
g2l->SetFillColor(kYellow);
g2l->Draw("3");
g2u->SetFillColor(kYellow);
g2u->Draw("3");
g1->SetFillColor(kGreen);
g1->Draw("3");
g0->SetLineColor(kBlue);
g0->SetLineStyle(2);
g0->SetLineWidth(2);
g0->Draw("L");
//g1->Draw("P");
//g2->Draw("P");
g->SetLineWidth(2);
g->Draw("PL");
// GENA: two-sided interval
//double alpha = 1.-r->ConfidenceLevel();
// GENA: upper limit
double alpha = (1.-r->ConfidenceLevel())/2.0;
double x1 = g->GetXaxis()->GetXmin();
double x2 = g->GetXaxis()->GetXmax();
TLine * line = new TLine(x1, alpha, x2,alpha);
line->SetLineColor(kRed);
line->Draw();
// see the expected limit and -1 +1 sigma bands
// SamplingDistribution * limits = r->GetUpperLimitDistribution();
// std::cout << " expected limit (median) " << limits->InverseCDF(0.50) << std::endl;
// std::cout << " expected limit (-1 sig) " << limits->InverseCDF((ROOT::Math::normal_cdf(-1))) << std::endl;
// std::cout << " expected limit (+1 sig) " << limits->InverseCDF((ROOT::Math::normal_cdf(+1))) << std::endl;
tw.Print();
}
