pair<double,double> bkgEvPerGeV(RooWorkspace *work, int m_hyp, int cat, int spin=false){
RooRealVar *mass = (RooRealVar*)work->var("CMS_hgg_mass");
if (spin) mass = (RooRealVar*)work->var("mass");
mass->setRange(100,180);
RooAbsPdf *pdf = (RooAbsPdf*)work->pdf(Form("pdf_data_pol_model_8TeV_cat%d",cat));
RooAbsData *data = (RooDataSet*)work->data(Form("data_mass_cat%d",cat));
RooPlot *tempFrame = mass->frame();
data->plotOn(tempFrame,Binning(80));
pdf->plotOn(tempFrame);
RooCurve *curve = (RooCurve*)tempFrame->getObject(tempFrame->numItems()-1);
double nombkg = curve->Eval(double(m_hyp));
RooRealVar *nlim = new RooRealVar(Form("nlim%d",cat),"",0.,0.,1.e5);
//double lowedge = tempFrame->GetXaxis()->GetBinLowEdge(FindBin(double(m_hyp)));
//double upedge = tempFrame->GetXaxis()->GetBinUpEdge(FindBin(double(m_hyp)));
//double center = tempFrame->GetXaxis()->GetBinUpCenter(FindBin(double(m_hyp)));
nlim->setVal(nombkg);
mass->setRange("errRange",m_hyp-0.5,m_hyp+0.5);
RooAbsPdf *epdf = 0;
epdf = new RooExtendPdf("epdf","",*pdf,*nlim,"errRange");
RooAbsReal *nll = epdf->createNLL(*data,Extended(),NumCPU(4));
RooMinimizer minim(*nll);
minim.setStrategy(0);
minim.setPrintLevel(-1);
minim.migrad();
minim.minos(*nlim);
double error = (nlim->getErrorLo(),nlim->getErrorHi())/2.;
data->Print();
return pair<double,double>(nombkg,error);
}
void compute_p0(const char* inFileName,
const char* wsName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData",
const char* asimov1DataName = "asimovData_1",
const char* conditional1Snapshot = "conditionalGlobs_1",
const char* nominalSnapshot = "nominalGlobs",
string smass = "130",
string folder = "test")
{
double mass;
stringstream massStr;
massStr << smass;
massStr >> mass;
double mu_profile_value = 1; // mu value to profile the obs data at wbefore generating the expected
bool doConditional = 1; // do conditional expected data
bool remakeData = 0; // handle unphysical pdf cases in H->ZZ->4l
bool doUncap = 1; // uncap p0
bool doInj = 0; // setup the poi for injection study (zero is faster if you're not)
bool doObs = 1; // compute median significance
bool doMedian = 1; // compute observed significance
TStopwatch timer;
timer.Start();
TFile f(inFileName);
RooWorkspace* ws = (RooWorkspace*)f.Get(wsName);
if (!ws)
{
cout << "ERROR::Workspace: " << wsName << " doesn't exist!" << endl;
return;
}
ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName);
if (!mc)
{
cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl;
return;
}
RooDataSet* data = (RooDataSet*)ws->data(dataName);
if (!data)
{
cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl;
return;
}
mc->GetNuisanceParameters()->Print("v");
ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2");
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(1);
cout << "Setting max function calls" << endl;
ws->loadSnapshot("conditionalNuis_0");
RooArgSet nuis(*mc->GetNuisanceParameters());
RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first();
RooAbsPdf* pdf = mc->GetPdf();
string condSnapshot(conditional1Snapshot);
RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters();
RooNLLVar* obs_nll = doObs ? (RooNLLVar*)pdf->createNLL(*data, Constrain(nuis_tmp2)) : NULL;
RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName);
RooRealVar* emb = (RooRealVar*)mc->GetNuisanceParameters()->find("ATLAS_EMB");
if (!asimovData1 || (string(inFileName).find("ic10") != string::npos && emb))
{
if (emb) emb->setVal(0.7);
cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
string mu_str, mu_prof_str;
asimovData1 = makeAsimovData(mc, doConditional, ws, obs_nll, 1, &mu_str, &mu_prof_str, mu_profile_value, true);
condSnapshot="conditionalGlobs"+mu_prof_str;
}
if (!doUncap) mu->setRange(0, 40);
else mu->setRange(-40, 40);
RooAbsPdf* pdf = mc->GetPdf();
RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters();
RooNLLVar* asimov_nll = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp1));
//do asimov
mu->setVal(1);
mu->setConstant(0);
if (!doInj) mu->setConstant(1);
int status,sign;
double med_sig=0,obs_sig=0,asimov_q0=0,obs_q0=0;
if (doMedian)
{
ws->loadSnapshot(condSnapshot.c_str());
if (doInj) ws->loadSnapshot("conditionalNuis_inj");
else ws->loadSnapshot("conditionalNuis_1");
mc->GetGlobalObservables()->Print("v");
mu->setVal(0);
mu->setConstant(1);
status = minimize(asimov_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov_nll_cond = asimov_nll->getVal();
mu->setVal(1);
if (doInj) ws->loadSnapshot("conditionalNuis_inj");
else ws->loadSnapshot("conditionalNuis_1");
if (doInj) mu->setConstant(0);
status = minimize(asimov_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov_nll_min = asimov_nll->getVal();
asimov_q0 = 2*(asimov_nll_cond - asimov_nll_min);
if (doUncap && mu->getVal() < 0) asimov_q0 = -asimov_q0;
sign = int(asimov_q0 != 0 ? asimov_q0/fabs(asimov_q0) : 0);
med_sig = sign*sqrt(fabs(asimov_q0));
ws->loadSnapshot(nominalSnapshot);
}
if (doObs)
{
ws->loadSnapshot("conditionalNuis_0");
mu->setVal(0);
mu->setConstant(1);
status = minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll_cond = obs_nll->getVal();
mu->setConstant(0);
status = minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll_min = obs_nll->getVal();
obs_q0 = 2*(obs_nll_cond - obs_nll_min);
if (doUncap && mu->getVal() < 0) obs_q0 = -obs_q0;
sign = int(obs_q0 == 0 ? 0 : obs_q0 / fabs(obs_q0));
if (!doUncap && (obs_q0 < 0 && obs_q0 > -0.1 || mu->getVal() < 0.001)) obs_sig = 0;
else obs_sig = sign*sqrt(fabs(obs_q0));
}
// Report results
cout << "obs: " << obs_sig << endl;
cout << "Observed significance: " << obs_sig << endl;
cout << "Corresponding to a p-value of " << (1-ROOT::Math::gaussian_cdf( obs_sig )) << endl;
if (med_sig)
{
cout << "Median test stat val: " << asimov_q0 << endl;
cout << "Median significance: " << med_sig << endl;
}
f.Close();
stringstream fileName;
fileName << "root-files/" << folder << "/" << mass << ".root";
system(("mkdir -vp root-files/" + folder).c_str());
TFile f2(fileName.str().c_str(),"recreate");
TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2);
h_hypo->SetBinContent(1, obs_sig);
h_hypo->SetBinContent(2, med_sig);
f2.Write();
f2.Close();
timer.Stop();
timer.Print();
}
int DiagnosisMacro(int Nbins = 10, int Nsigma = 10, int CPUused = 1, TString Filename = "FIT_DATA_Psi2SJpsi_PPPrompt_Bkg_SecondOrderChebychev_pt65300_rap016_cent0200_262620_263757.root", TString Outputdir = "./")
//Nbins: Number of points for which to calculate profile likelihood. Time required is about (1/CPU) minutes per point per parameter. 0 means do plain likelihood only
//Nsigma: The range in which the scan is performed (value-Nsigma*sigma, value+Nsigma*sigma)
//CPUused: anything larger than 1 causes weird fit results on my laptop, runs fine on lxplus with more (16)
{
// R e a d w o r k s p a c e f r o m f i l e
// -----------------------------------------------
// Open input file with workspace
//Filename = "FIT_DATA_Psi2SJpsi_PP_Jpsi_DoubleCrystalBall_Psi2S_DoubleCrystalBall_Bkg_Chebychev2_pt6590_rap016_cent0200.root";
//Filename = "FIT_DATA_Psi2SJpsi_PbPb_Jpsi_DoubleCrystalBall_Psi2S_DoubleCrystalBall_Bkg_Chebychev1_pt6590_rap016_cent0200.root";
TFile *f = new TFile(Filename);
// Retrieve workspace from file
RooWorkspace* w = (RooWorkspace*)f->Get("workspace");
// Retrieve x,model and data from workspace
RooRealVar* x = w->var("invMass");
RooAbsPdf* model = w->pdf("simPdf_syst");
if (model == 0) {
model = w->pdf("simPdf");
}
if (model == 0) {
model = w->pdf("pdfMASS_Tot_PP");
}
if (model == 0) {
model = w->pdf("pdfMASS_Tot_PbPb");
}
if (model == 0) {
cout << "[ERROR] pdf failed to load from the workspace" << endl;
return false;
}
RooAbsData* data = w->data("dOS_DATA");
if (data == 0) {
data = w->data("dOS_DATA_PP");
}
if (data == 0) {
data = w->data("dOS_DATA_PbPb");
}
if (data == 0) {
cout << "[ERROR] data failed to load from the workspace" << endl;
return false;
}
// Print structure of composite p.d.f.
model->Print("t");
/*
// P l o t m o d e l
// ---------------------------------------------------------
// Plot data and PDF overlaid
RooPlot* xframe = x->frame(Title("J/psi Model and Data"));
data->plotOn(xframe);
model->plotOn(xframe);
// Draw the frame on the canvas
TCanvas* c2 = new TCanvas("PlotModel", "PlotModel", 1000, 1000);
gPad->SetLeftMargin(0.15);
xframe->GetYaxis()->SetTitleOffset(2.0);
xframe->Draw();//*/
///// Check parameters
RooArgSet* paramSet1 = model->getDependents(data);
paramSet1->Print("v"); // Just check
RooArgSet* paramSet2 = model->getParameters(data);
paramSet2->Print("v");
int Nparams = paramSet2->getSize();
cout << "Number of parameters: " << Nparams<<endl<<endl;
// C o n s t r u c t p l a i n l i k e l i h o o d
// ---------------------------------------------------
// Construct unbinned likelihood
RooAbsReal* nll = model->createNLL(*data, NumCPU(CPUused));
// Minimize likelihood w.r.t all parameters before making plots
RooMinuit(*nll).migrad();
//////////////////////////////////////////////////////
/////////////////// L O O P O V E R P A R A M E T E R S
/////////////////////////////////////////////////////
/// Set up loop over parameters
TString ParamName;
double ParamValue;
double ParamError;
double ParamLimitLow;
double ParamLimitHigh;
double FitRangeLow;
double FitRangeHigh;
RooRealVar* vParam;
int counter = 0;
// Loop start
TIterator* iter = paramSet2->createIterator();
TObject* var = iter->Next();
while (var != 0) {
counter++;
ParamName = var->GetName();
vParam = w->var(ParamName);
ParamValue = vParam->getVal();
ParamError = vParam->getError();
ParamLimitLow = vParam->getMin();
ParamLimitHigh = vParam->getMax();
cout << ParamName << " has value " << ParamValue << " with error: " << ParamError << " and limits: " << ParamLimitLow << " to " << ParamLimitHigh << endl << endl;
if (ParamError == 0) { //Skipping fixed parameters
cout << "Parameter was fixed, skipping its fitting" << endl;
cout << endl << "DONE WITH " << counter << " PARAMETER OUT OF " << Nparams << endl << endl;
var = iter->Next();
continue;
}
// determining fit range: Nsigma sigma on each side unless it would be outside of parameter limits
if ((ParamValue - Nsigma * ParamError) > ParamLimitLow) {
FitRangeLow = (ParamValue - Nsigma * ParamError);
}
else {
FitRangeLow = ParamLimitLow;
}
if ((ParamValue + Nsigma * ParamError) < ParamLimitHigh) {
FitRangeHigh = (ParamValue + Nsigma * ParamError);
}
else {
FitRangeHigh = ParamLimitHigh;
}
// P l o t p l a i n l i k e l i h o o d a n d C o n s t r u c t p r o f i l e l i k e l i h o o d
// ---------------------------------------------------
RooPlot* frame1;
RooAbsReal* pll=NULL;
if (Nbins != 0) {
frame1 = vParam->frame(Bins(Nbins), Range(FitRangeLow, FitRangeHigh), Title(TString::Format("LL and profileLL in %s", ParamName.Data())));
nll->plotOn(frame1, ShiftToZero());
pll = nll->createProfile(*vParam);
// Plot the profile likelihood
pll->plotOn(frame1, LineColor(kRed), RooFit::Precision(-1));
}
else { //Skip profile likelihood
frame1 = vParam->frame(Bins(10), Range(FitRangeLow, FitRangeHigh), Title(TString::Format("LL and profileLL in %s", ParamName.Data())));
nll->plotOn(frame1, ShiftToZero());
}
// D r a w a n d s a v e p l o t s
// -----------------------------------------------------------------------
// Adjust frame maximum for visual clarity
frame1->SetMinimum(0);
frame1->SetMaximum(20);
TCanvas* c = new TCanvas("CLikelihoodResult", "CLikelihoodResult", 800, 600);
c->cd(1);
gPad->SetLeftMargin(0.15);
frame1->GetYaxis()->SetTitleOffset(1.4);
frame1->Draw();
TLegend* leg = new TLegend(0.70, 0.70, 0.95, 0.88, "");
leg->SetFillColor(kWhite);
leg->SetBorderSize(0);
leg->SetTextSize(0.035);
TLegendEntry *le1 = leg->AddEntry(nll, "Plain likelihood", "l");
le1->SetLineColor(kBlue);
le1->SetLineWidth(3);
TLegendEntry *le2 = leg->AddEntry(pll, "Profile likelihood", "l");
le2->SetLineColor(kRed);
le2->SetLineWidth(3);
leg->Draw("same");
//Save plot
TString StrippedName = TString(Filename(Filename.Last('/')+1,Filename.Length()));
StrippedName = StrippedName.ReplaceAll(".root","");
cout << StrippedName << endl;
gSystem->mkdir(Form("%s/root/%s", Outputdir.Data(), StrippedName.Data()), kTRUE);
c->SaveAs(Form("%s/root/%s/Likelihood_scan_%s.root", Outputdir.Data(), StrippedName.Data(), ParamName.Data()));
gSystem->mkdir(Form("%s/pdf/%s", Outputdir.Data(), StrippedName.Data()), kTRUE);
c->SaveAs(Form("%s/pdf/%s/Likelihood_scan_%s.pdf", Outputdir.Data(), StrippedName.Data(), ParamName.Data()));
gSystem->mkdir(Form("%s/png/%s", Outputdir.Data(), StrippedName.Data()), kTRUE);
c->SaveAs(Form("%s/png/%s/Likelihood_scan_%s.png", Outputdir.Data(), StrippedName.Data(), ParamName.Data()));
delete c;
delete frame1;
if (pll) delete pll;
cout << endl << "DONE WITH " << counter << " PARAMETER OUT OF " << Nparams << endl << endl;
//if (counter == 2){ break; } //Exit - for testing
var = iter->Next();
} // End of the loop
return true;
}
void runQ(const char* inFileName,
const char* wsName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData",
const char* asimov0DataName = "asimovData_0",
const char* conditional0Snapshot = "conditionalGlobs_0",
const char* asimov1DataName = "asimovData_1",
const char* conditional1Snapshot = "conditionalGlobs_1",
const char* nominalSnapshot = "nominalGlobs",
string smass = "130",
string folder = "test")
{
double mass;
stringstream massStr;
massStr << smass;
massStr >> mass;
bool errFast = 0;
bool goFast = 1;
bool remakeData = 1;
bool doRightSided = 1;
bool doInj = 0;
bool doObs = 1;
bool doMedian = 1;
TStopwatch timer;
timer.Start();
TFile f(inFileName);
RooWorkspace* ws = (RooWorkspace*)f.Get(wsName);
if (!ws)
{
cout << "ERROR::Workspace: " << wsName << " doesn't exist!" << endl;
return;
}
ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName);
if (!mc)
{
cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl;
return;
}
RooDataSet* data = (RooDataSet*)ws->data(dataName);
if (!data)
{
cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl;
return;
}
mc->GetNuisanceParameters()->Print("v");
RooNLLVar::SetIgnoreZeroEntries(1);
ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2");
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(1);
cout << "Setting max function calls" << endl;
//ROOT::Math::MinimizerOptions::SetDefaultMaxFunctionCalls(20000);
RooMinimizer::SetMaxFunctionCalls(10000);
ws->loadSnapshot("conditionalNuis_0");
RooArgSet nuis(*mc->GetNuisanceParameters());
RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first();
if (string(mc->GetPdf()->ClassName()) == "RooSimultaneous" && remakeData)
{
RooSimultaneous* simPdf = (RooSimultaneous*)mc->GetPdf();
double min_mu;
data = makeData(data, simPdf, mc->GetObservables(), mu, mass, min_mu);
}
RooDataSet* asimovData0 = (RooDataSet*)ws->data(asimov0DataName);
if (!asimovData0)
{
cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
makeAsimovData(mc, true, ws, mc->GetPdf(), data, 1);
ws->Print();
asimovData0 = (RooDataSet*)ws->data("asimovData_0");
}
RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName);
if (!asimovData1)
{
cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
makeAsimovData(mc, true, ws, mc->GetPdf(), data, 0);
ws->Print();
asimovData1 = (RooDataSet*)ws->data("asimovData_1");
}
if (!doRightSided) mu->setRange(0, 40);
else mu->setRange(-40, 40);
bool old = false;
if (old)
{
mu->setVal(0);
RooArgSet poi(*mu);
ProfileLikelihoodTestStat_modified asimov_testStat_sig(*mc->GetPdf());
asimov_testStat_sig.SetRightSided(doRightSided);
asimov_testStat_sig.SetNuis(&nuis);
if (!doInj) asimov_testStat_sig.SetDoAsimov(true, 1);
asimov_testStat_sig.SetWorkspace(ws);
ProfileLikelihoodTestStat_modified testStat(*mc->GetPdf());
testStat.SetRightSided(doRightSided);
testStat.SetNuis(&nuis);
testStat.SetWorkspace(ws);
//RooMinimizerFcn::SetOverrideEverything(true);
double med_sig = 0;
double med_testStat_val = 0;
//gRandom->SetSeed(1);
//RooRandom::randomGenerator()->SetSeed(1);
RooNLLVar::SetIgnoreZeroEntries(1);
if (asimov1DataName != "" && doMedian)
{
mu->setVal(0);
if (!doInj) mu->setRange(0, 2);
ws->loadSnapshot("conditionalNuis_0");
asimov_testStat_sig.SetLoadUncondSnapshot("conditionalNuis_1");
if (string(conditional1Snapshot) != "") ws->loadSnapshot(conditional1Snapshot);
med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi);
if (med_testStat_val < 0 && !doInj)
{
mu->setVal(0);
med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi); // just try again
}
int sign = med_testStat_val != 0 ? med_testStat_val/fabs(med_testStat_val) : 0;
med_sig = sign*sqrt(fabs(med_testStat_val));
if (string(nominalSnapshot) != "") ws->loadSnapshot(nominalSnapshot);
if (!doRightSided) mu->setRange(0, 40);
else mu->setRange(-40, 40);
}
RooNLLVar::SetIgnoreZeroEntries(0);
//gRandom->SetSeed(1);
//RooRandom::randomGenerator()->SetSeed(1);
//RooMinimizerFcn::SetOverrideEverything(false);
cout << "med test stat: " << med_testStat_val << endl;
ws->loadSnapshot("nominalGlobs");
ws->loadSnapshot("conditionalNuis_0");
mu->setVal(0);
testStat.SetWorkspace(ws);
testStat.SetLoadUncondSnapshot("ucmles");
double obsTestStat_val = doObs ? 2*testStat.Evaluate(*data, poi) : 0;
cout << "obs test stat: " << obsTestStat_val << endl;
// obsTestStat_val = 2*testStat.Evaluate(*data, poi);
// cout << "obs test stat: " << obsTestStat_val << endl;
// obsTestStat_val = 2*testStat.Evaluate(*data, poi);
// cout << "obs test stat: " << obsTestStat_val << endl;
double obs_sig;
int sign = obsTestStat_val == 0 ? 0 : obsTestStat_val / fabs(obsTestStat_val);
if (!doRightSided && (obsTestStat_val < 0 && obsTestStat_val > -0.1 || mu->getVal() < 0.001)) obs_sig = 0;
else obs_sig = sign*sqrt(fabs(obsTestStat_val));
if (obs_sig != obs_sig) //nan, do by hand
{
cout << "Obs test stat gave nan: try by hand" << endl;
mu->setVal(0);
mu->setConstant(1);
mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters()));
mu->setConstant(0);
double L_0 = mc->GetPdf()->getVal();
//mu->setVal(0);
//mu->setConstant(1);
mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters()));
//mu->setConstant(0);
double L_muhat = mc->GetPdf()->getVal();
cout << "L_0: " << L_0 << ", L_muhat: " << L_muhat << endl;
obs_sig = sqrt(-2*TMath::Log(L_0/L_muhat));
//still nan
if (obs_sig != obs_sig && fabs(L_0 - L_muhat) < 0.000001) obs_sig = 0;
}
cout << "obs: " << obs_sig << endl;
cout << "Observed significance: " << obs_sig << endl;
if (med_sig)
{
cout << "Median test stat val: " << med_testStat_val << endl;
cout << "Median significance: " << med_sig << endl;
}
f.Close();
stringstream fileName;
fileName << "root_files/" << folder << "/" << mass << ".root";
system(("mkdir -vp root_files/" + folder).c_str());
TFile f2(fileName.str().c_str(),"recreate");
// stringstream fileName;
// fileName << "results_sig/" << mass << ".root";
// system("mkdir results_sig");
// TFile f(fileName.str().c_str(),"recreate");
TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2);
h_hypo->SetBinContent(1, obs_sig);
h_hypo->SetBinContent(2, med_sig);
f2.Write();
f2.Close();
//mc->GetPdf()->fitTo(*data, PrintLevel(0));
timer.Stop();
timer.Print();
}
else
{
RooAbsPdf* pdf = mc->GetPdf();
RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters();
RooNLLVar* asimov_nll0 = (RooNLLVar*)pdf->createNLL(*asimovData0, Constrain(nuis_tmp1));
RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters();
RooNLLVar* asimov_nll1 = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp2));
RooArgSet nuis_tmp3 = *mc->GetNuisanceParameters();
RooNLLVar* obs_nll = (RooNLLVar*)pdf->createNLL(*data, Constrain(nuis_tmp3));
//do asimov
int status;
//get sigma_b
ws->loadSnapshot(conditional0Snapshot);
status = ws->loadSnapshot("conditionalNuis_0");
if (status != 0 && goFast) errFast = 1;
mu->setVal(0);
mu->setConstant(1);
status = goFast ? 0 : minimize(asimov_nll0, ws);
if (status < 0)
{
cout << "Retrying" << endl;
//ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll0, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov0_nll0 = asimov_nll0->getVal();
mu->setVal(1);
ws->loadSnapshot("conditionalNuis_1");
status = minimize(asimov_nll0, ws);
if (status < 0)
{
cout << "Retrying" << endl;
//ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll0, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov0_nll1 = asimov_nll0->getVal();
double asimov0_q = 2*(asimov0_nll1 - asimov0_nll0);
double sigma_b = sqrt(1./asimov0_q);
ws->loadSnapshot(nominalSnapshot);
//get sigma_sb
ws->loadSnapshot(conditional1Snapshot);
ws->loadSnapshot("conditionalNuis_0");
mu->setVal(0);
mu->setConstant(1);
status = minimize(asimov_nll1, ws);
if (status < 0)
{
cout << "Retrying" << endl;
//ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll1, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov1_nll0 = asimov_nll1->getVal();
mu->setVal(1);
status = ws->loadSnapshot("conditionalNuis_1");
if (status != 0 && goFast) errFast = 1;
status = goFast ? 0 : minimize(asimov_nll1, ws);
if (status < 0)
{
cout << "Retrying" << endl;
//ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll1, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov1_nll1 = asimov_nll1->getVal();
double asimov1_q = 2*(asimov1_nll1 - asimov1_nll0);
double sigma_sb = sqrt(-1./asimov1_q);
ws->loadSnapshot(nominalSnapshot);
//do obs
mu->setVal(0);
status = ws->loadSnapshot("conditionalNuis_0");
if (status != 0 && goFast) errFast = 1;
mu->setConstant(1);
status = goFast ? 0 : minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll0 = obs_nll->getVal();
status = ws->loadSnapshot("conditionalNuis_1");
if (status != 0 && goFast) errFast = 1;
mu->setVal(1);
status = goFast ? 0 : minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll1 = obs_nll->getVal();
double obs_q = 2*(obs_nll1 - obs_nll0);
double Zobs = (1./sigma_b/sigma_b - obs_q) / (2./sigma_b);
double Zexp = (1./sigma_b/sigma_b - asimov1_q) / (2./sigma_b);
double pb_obs = 1-ROOT::Math::gaussian_cdf(Zobs);
double pb_exp = 1-ROOT::Math::gaussian_cdf(Zexp);
cout << "asimov0_q = " << asimov0_q << endl;
cout << "asimov1_q = " << asimov1_q << endl;
cout << "obs_q = " << obs_q << endl;
cout << "sigma_b = " << sigma_b << endl;
cout << "sigma_sb = " << sigma_sb << endl;
cout << "Z obs = " << Zobs << endl;
cout << "Z exp = " << Zexp << endl;
f.Close();
stringstream fileName;
fileName << "root_files/" << folder << "/" << mass << ".root";
system(("mkdir -vp root_files/" + folder).c_str());
TFile f2(fileName.str().c_str(),"recreate");
TH1D* h_hypo = new TH1D("hypo_tev","hypo_tev",2,0,2);
h_hypo->SetBinContent(1, pb_obs);
h_hypo->SetBinContent(2, pb_exp);
f2.Write();
f2.Close();
stringstream fileName3;
fileName3 << "root_files/" << folder << "_llr/" << mass << ".root";
system(("mkdir -vp root_files/" + folder + "_llr").c_str());
TFile f3(fileName3.str().c_str(),"recreate");
TH1D* h_hypo3 = new TH1D("hypo_llr","hypo_llr",7,0,7);
h_hypo3->SetBinContent(1, -obs_q);
h_hypo3->SetBinContent(2, -asimov1_q);
h_hypo3->SetBinContent(3, -asimov0_q);
h_hypo3->SetBinContent(4, -asimov0_q-2*2/sigma_b);
h_hypo3->SetBinContent(5, -asimov0_q-1*2/sigma_b);
h_hypo3->SetBinContent(6, -asimov0_q+1*2/sigma_b);
h_hypo3->SetBinContent(7, -asimov0_q+2*2/sigma_b);
f3.Write();
f3.Close();
timer.Stop();
timer.Print();
}
}
void results2tree(
const char* workDirName,
bool isMC=false,
const char* thePoiNames="RFrac2Svs1S,N_Jpsi,f_Jpsi,m_Jpsi,sigma1_Jpsi,alpha_Jpsi,n_Jpsi,sigma2_Jpsi,MassRatio,rSigma21_Jpsi,lambda1_Bkg,lambda2_Bkg,lambda3_Bkg,lambda4_Bkg,lambda5__Bkg,N_Bkg"
) {
// workDirName: usual tag where to look for files in Output
// thePoiNames: comma-separated list of parameters to store ("par1,par2,par3"). Default: all
TFile *f = new TFile(treeFileName(workDirName,isMC),"RECREATE");
TTree *tr = new TTree("fitresults","fit results");
// bin edges
float ptmin, ptmax, ymin, ymax, centmin, centmax;
// model names
Char_t jpsiName[128], psipName[128], bkgName[128];
// collision system
Char_t collSystem[8];
// goodness of fit
float nll, chi2, normchi2; int npar, ndof;
// parameters to store: make it a vector
vector<poi> thePois;
TString thePoiNamesStr(thePoiNames);
TString t; Int_t from = 0;
while (thePoiNamesStr.Tokenize(t, from , ",")) {
poi p; strcpy(p.name, t.Data());
cout << p.name << endl;
thePois.push_back(p);
}
// create tree branches
tr->Branch("ptmin",&ptmin,"ptmin/F");
tr->Branch("ptmax",&ptmax,"ptmax/F");
tr->Branch("ymin",&ymin,"ymin/F");
tr->Branch("ymax",&ymax,"ymax/F");
tr->Branch("centmin",¢min,"centmin/F");
tr->Branch("centmax",¢max,"centmax/F");
tr->Branch("jpsiName",jpsiName,"jpsiName/C");
tr->Branch("psipName",psipName,"psipName/C");
tr->Branch("bkgName",bkgName,"bkgName/C");
tr->Branch("collSystem",collSystem,"collSystem/C");
tr->Branch("nll",&nll,"nll/F");
tr->Branch("chi2",&chi2,"chi2/F");
tr->Branch("normchi2",&normchi2,"normchi2/F");
tr->Branch("npar",&npar,"npar/I");
tr->Branch("ndof",&ndof,"ndof/I");
for (vector<poi>::iterator it=thePois.begin(); it!=thePois.end(); it++) {
tr->Branch(Form("%s_val",it->name),&(it->val),Form("%s_val/F",it->name));
tr->Branch(Form("%s_err",it->name),&(it->err),Form("%s_err/F",it->name));
}
// list of files
vector<TString> theFiles = fileList(workDirName,"",isMC);
int cnt=0;
for (vector<TString>::const_iterator it=theFiles.begin(); it!=theFiles.end(); it++) {
cout << "Parsing file " << cnt << " / " << theFiles.size() << ": " << *it << endl;
// parse the file name to get info
anabin thebin = binFromFile(*it);
ptmin = thebin.ptbin().low();
ptmax = thebin.ptbin().high();
ymin = thebin.rapbin().low();
ymax = thebin.rapbin().high();
centmin = thebin.centbin().low();
centmax = thebin.centbin().high();
strcpy(collSystem, (it->Index("PbPb")>0) ? "PbPb" : "PP");
// get the model names
from = 0;
bool catchjpsi=false, catchpsip=false, catchbkg=false;
while (it->Tokenize(t, from, "_")) {
if (catchjpsi) {strcpy(jpsiName, t.Data()); catchjpsi=false;}
if (catchpsip) {strcpy(psipName, t.Data()); catchpsip=false;}
if (catchbkg) {strcpy(bkgName, t.Data()); catchbkg=false;}
if (t=="Jpsi") catchjpsi=true;
if (t=="Psi2S") catchpsip=true;
if (t=="Bkg") catchbkg=true;
}
TFile *f = new TFile(*it); RooWorkspace *ws = NULL;
if (!f) {
cout << "Error, file " << *it << " does not exist." << endl;
} else {
ws = (RooWorkspace*) f->Get("workspace");
if (!ws) {
cout << "Error, workspace not found in " << *it << "." << endl;
}
}
nll=0; chi2=0; npar=0; ndof=0;
if (f && ws) {
// get the model for nll and npar
RooAbsPdf *model = pdfFromWS(ws, Form("_%s",collSystem), "pdfMASS_Tot");
if (model) {
RooAbsData *dat = dataFromWS(ws, Form("_%s",collSystem), "dOS_DATA");
if (dat) {
RooAbsReal *NLL = model->createNLL(*dat);
if (NLL) nll = NLL->getVal();
npar = model->getParameters(dat)->selectByAttrib("Constant",kFALSE)->getSize();
// compute the chi2 and the ndof
RooPlot* frame = ws->var("invMass")->frame(Bins(nBins));
dat->plotOn(frame);
model->plotOn(frame);
TH1 *hdatact = dat->createHistogram("hdatact", *(ws->var("invMass")), Binning(nBins));
RooHist *hpull = frame->pullHist(0,0, true);
double* ypulls = hpull->GetY();
unsigned int nFullBins = 0;
for (int i = 0; i < nBins; i++) {
if (hdatact->GetBinContent(i+1) > 0.0) {
chi2 += ypulls[i]*ypulls[i];
nFullBins++;
}
}
ndof = nFullBins - npar;
normchi2 = chi2/ndof;
}
}
// get the POIs
for (vector<poi>::iterator itpoi=thePois.begin(); itpoi!=thePois.end(); itpoi++) {
RooRealVar *thevar = poiFromWS(ws, Form("_%s",collSystem), itpoi->name);
itpoi->val = thevar ? thevar->getVal() : 0;
itpoi->err = thevar ? thevar->getError() : 0;
}
f->Close();
delete f;
} else {
for (vector<poi>::iterator itpoi=thePois.begin(); itpoi!=thePois.end(); itpoi++) {
itpoi->val = 0;
itpoi->err = 0;
}
}
// fill the tree
tr->Fill();
cnt++;
} // loop on the files
f->Write();
f->Close();
}
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();
*/
}
void fitbkgdataCard(TString configCard="template.config",
bool dobands = true, // create baerror bands for BG models
bool dosignal = false, // plot the signal model (needs to be present)
bool blinded = true, // blind the data in the plots?
bool verbose = true ) {
gROOT->Macro("MitStyle.C");
gStyle->SetErrorX(0);
gStyle->SetOptStat(0);
gROOT->ForceStyle();
TString projectDir;
std::vector<TString> catdesc;
std::vector<TString> catnames;
std::vector<int> polorder;
double massmin = -1.;
double massmax = -1.;
double theCMenergy = -1.;
bool readStatus = readFromConfigCard( configCard,
projectDir,
catnames,
catdesc,
polorder,
massmin,
massmax,
theCMenergy
);
if( !readStatus ) {
std::cerr<<" ERROR: Could not read from card > "<<configCard.Data()<<" <."<<std::endl;
return;
}
TFile *fdata = new TFile(TString::Format("%s/CMS-HGG-data.root",projectDir.Data()),"READ");
if( !fdata ) {
std::cerr<<" ERROR: Could not open file "<<projectDir.Data()<<"/CMS-HGG-data.root."<<std::endl;
return;
}
if( !gSystem->cd(TString::Format("%s/databkg/",projectDir.Data())) ) {
std::cerr<<" ERROR: Could not change directory to "<<TString::Format("%s/databkg/",projectDir.Data()).Data()<<"."<<std::endl;
return;
}
// ----------------------------------------------------------------------
// load the input workspace....
RooWorkspace* win = (RooWorkspace*)fdata->Get("cms_hgg_workspace_data");
if( !win ) {
std::cerr<<" ERROR: Could not load workspace > cms_hgg_workspace_data < from file > "<<TString::Format("%s/CMS-HGG-data.root",projectDir.Data()).Data()<<" <."<<std::endl;
return;
}
RooRealVar *intLumi = win->var("IntLumi");
RooRealVar *hmass = win->var("CMS_hgg_mass");
if( !intLumi || !hmass ) {
std::cerr<<" ERROR: Could not load needed variables > IntLumi < or > CMS_hgg_mass < forom input workspace."<<std::endl;
return;
}
//win->Print();
hmass->setRange(massmin,massmax);
hmass->setBins(4*(int)(massmax-massmin));
hmass->SetTitle("m_{#gamma#gamma}");
hmass->setUnit("GeV");
hmass->setRange("fitrange",massmin,massmax);
hmass->setRange("blind1",100.,110.);
hmass->setRange("blind2",150.,180.);
// ----------------------------------------------------------------------
// some auxiliray vectro (don't know the meaning of all of them ... yet...
std::vector<RooAbsData*> data_vec;
std::vector<RooAbsPdf*> pdfShape_vec; // vector to store the NOT-EXTENDED PDFs (aka pdfshape)
std::vector<RooAbsPdf*> pdf_vec; // vector to store the EXTENDED PDFs
std::vector<RooAbsReal*> normu_vec; // this holds the normalization vars for each Cat (needed in bands for combined cat)
RooArgList normList; // list of range-limityed normalizations (needed for error bands on combined category)
//std::vector<RooRealVar*> coeffv;
//std::vector<RooAbsReal*> normu_vecv; // ???
// ----------------------------------------------------------------------
// define output works
RooWorkspace *wOut = new RooWorkspace("wbkg","wbkg") ;
// util;ities for the combined fit
RooCategory finalcat ("finalcat", "finalcat") ;
RooSimultaneous fullbkgpdf("fullbkgpdf","fullbkgpdf",finalcat);
RooDataSet datacomb ("datacomb", "datacomb", RooArgList(*hmass,finalcat)) ;
RooDataSet *datacombcat = new RooDataSet("data_combcat","",RooArgList(*hmass)) ;
// add the 'combcat' to the list...if more than one cat
if( catnames.size() > 1 ) {
catnames.push_back("combcat");
catdesc.push_back("Combined");
}
for (UInt_t icat=0; icat<catnames.size(); ++icat) {
TString catname = catnames.at(icat);
finalcat.defineType(catname);
// check if we're in a sub-cat or the comb-cat
RooDataSet *data = NULL;
RooDataSet *inData = NULL;
if( icat < (catnames.size() - 1) || catnames.size() == 1) { // this is NOT the last cat (which is by construction the combination)
inData = (RooDataSet*)win->data(TString("data_mass_")+catname);
if( !inData ) {
std::cerr<<" ERROR: Could not find dataset > data_mass_"<<catname.Data()<<" < in input workspace."<<std::endl;
return;
}
data = new RooDataSet(TString("data_")+catname,"",*hmass,Import(*inData)); // copy the dataset (why?)
// append the data to the combined data...
RooDataSet *datacat = new RooDataSet(TString("datacat")+catname,"",*hmass,Index(finalcat),Import(catname,*data)) ;
datacomb.append(*datacat);
datacombcat->append(*data);
// normalization for this category
RooRealVar *nbkg = new RooRealVar(TString::Format("CMS_hgg_%s_bkgshape_norm",catname.Data()),"",800.0,0.0,25e3);
// we keep track of the normalizario vars only for N-1 cats, naming convetnions hystoric...
if( catnames.size() > 2 && icat < (catnames.size() - 2) ) {
RooRealVar* cbkg = new RooRealVar(TString::Format("cbkg%s",catname.Data()),"",0.0,0.0,1e3);
cbkg->removeRange();
normu_vec.push_back(cbkg);
normList.add(*cbkg);
}
/// generate the Bernstrin polynomial (FIX-ME: add possibility ro create other models...)
fstBernModel* theBGmodel = new fstBernModel(hmass, polorder[icat], icat, catname); // using my dedicated class...
std::cout<<" model name is "<<theBGmodel->getPdf()->GetName()<<std::endl;
RooAbsPdf* bkgshape = theBGmodel->getPdf(); // the BG shape
RooAbsPdf* bkgpdf = new RooExtendPdf(TString("bkgpdf")+catname,"",*bkgshape,*nbkg); // the extended PDF
// add the extedned PDF to the RooSimultaneous holding all models...
fullbkgpdf.addPdf(*bkgpdf,catname);
// store the NON-EXTENDED PDF for usgae to compute the error bands later..
pdfShape_vec.push_back(bkgshape);
pdf_vec .push_back(bkgpdf);
data_vec .push_back(data);
} else {
data = datacombcat; // we're looking at the last cat (by construction the combination)
data_vec.push_back(data);
// sum up all the cts PDFs for combined PDF
RooArgList subpdfs;
for (int ipdf=0; ipdf<pdf_vec.size(); ++ipdf) {
subpdfs.add(*pdf_vec.at(ipdf));
}
RooAddPdf* bkgpdf = new RooAddPdf(TString("bkgpdf")+catname,"",subpdfs);
pdfShape_vec.push_back(bkgpdf);
pdf_vec .push_back(bkgpdf); // I don't think this is really needed though....
}
// generate the binned dataset (to be put into the workspace... just in case...)
RooDataHist *databinned = new RooDataHist(TString("databinned_")+catname,"",*hmass,*data);
wOut->import(*data);
wOut->import(*databinned);
}
std::cout<<" ***************** "<<std::endl;
// fit the RooSimultaneous to the combined dataset -> (we could also fit each cat separately)
fullbkgpdf.fitTo(datacomb,Strategy(1),Minos(kFALSE),Save(kTRUE));
RooFitResult *fullbkgfitres = fullbkgpdf.fitTo(datacomb,Strategy(2),Minos(kFALSE),Save(kTRUE));
// in principle we're done now, so store the results in the output workspace
wOut->import(datacomb);
wOut->import(fullbkgpdf);
wOut->import(*fullbkgfitres);
std::cout<<" ***************** "<<std::endl;
if( verbose ) wOut->Print();
std::cout<<" ***************** "<<std::endl;
wOut->writeToFile("bkgdatawithfit.root") ;
if( verbose ) {
printf("IntLumi = %5f\n",intLumi->getVal());
printf("ndata:\n");
for (UInt_t icat=0; icat<catnames.size(); ++icat) {
printf("%i ",data_vec.at(icat)->numEntries());
}
printf("\n");
}
// --------------------------------------------------------------------------------------------
// Now comesd the plotting
// chage the Statistics style...
gStyle->SetOptStat(1110);
// we want to plot in 1GeV bins (apparently...)
UInt_t nbins = (UInt_t) (massmax-massmin);
// here we'll store the curves for the bands...
std::vector<RooCurve*> fitcurves;
// loop again over the cats
TCanvas **canbkg = new TCanvas*[catnames.size()];
RooPlot** plot = new RooPlot*[catnames.size()];
TLatex** lat = new TLatex*[catnames.size()];
TLatex** lat2 = new TLatex*[catnames.size()];
std::cout<<" beofre plotting..."<<std::endl;
for (UInt_t icat=0; icat<catnames.size(); ++icat) {
TString catname = catnames.at(icat);
std::cout<<" trying to plot #"<<icat<<std::endl;
// plot the data and the fit
canbkg[icat] = new TCanvas;
plot [icat] = hmass->frame(Bins(nbins),Range("fitrange"));
std::cout<<" trying to plot #"<<icat<<std::endl;
// first plot the data invisibly... and put the fitted BG model on top...
data_vec .at(icat)->plotOn(plot[icat],RooFit::LineColor(kWhite),MarkerColor(kWhite),Invisible());
pdfShape_vec.at(icat)->plotOn(plot[icat],RooFit::LineColor(kRed),Range("fitrange"),NormRange("fitrange"));
std::cout<<" trying to plot #"<<icat<<std::endl;
// if toggled on, plot also the Data visibly
if( !blinded ) {
data_vec.at(icat)->plotOn(plot[icat]);
}
std::cout<<" trying to plot #"<<icat<<std::endl;
// some cosmetics...
plot[icat]->SetTitle("");
plot[icat]->SetMinimum(0.0);
plot[icat]->SetMaximum(1.40*plot[icat]->GetMaximum());
plot[icat]->GetXaxis()->SetTitle("m_{#gamma#gamma} (GeV/c^{2})");
plot[icat]->Draw();
std::cout<<" trying to plot #"<<icat<<std::endl;
// legend....
TLegend *legmc = new TLegend(0.68,0.70,0.97,0.90);
legmc->AddEntry(plot[icat]->getObject(2),"Data","LPE");
legmc->AddEntry(plot[icat]->getObject(1),"Bkg Model","L");
// this part computes the 1/2-sigma bands.
TGraphAsymmErrors *onesigma = NULL;
TGraphAsymmErrors *twosigma = NULL;
std::cout<<" trying *** to plot #"<<icat<<std::endl;
RooAddition* sumcatsnm1 = NULL;
if ( dobands ) { //&& icat == (catnames.size() - 1) ) {
onesigma = new TGraphAsymmErrors();
twosigma = new TGraphAsymmErrors();
// get the PDF for this cat from the vector
RooAbsPdf *thisPdf = pdfShape_vec.at(icat);
// get the nominal fir curve
RooCurve *nomcurve = dynamic_cast<RooCurve*>(plot[icat]->getObject(1));
fitcurves.push_back(nomcurve);
bool iscombcat = ( icat == (catnames.size() - 1) && catnames.size() > 1);
RooAbsData *datanorm = ( iscombcat ? &datacomb : data_vec.at(icat) );
// this si the nornmalization in the 'sliding-window' (i.e. per 'test-bin')
RooRealVar *nlim = new RooRealVar(TString::Format("nlim%s",catnames.at(icat).Data()),"",0.0,0.0,10.0);
nlim->removeRange();
if( iscombcat ) {
// ----------- HISTORIC NAMING ----------------------------------------
sumcatsnm1 = new RooAddition("sumcatsnm1","",normList); // summing all normalizations epect the last Cat
// this is the normlization of the last Cat
RooFormulaVar *nlast = new RooFormulaVar("nlast","","TMath::Max(0.1,@0-@1)",RooArgList(*nlim,*sumcatsnm1));
// ... and adding it ot the list of norms
normu_vec.push_back(nlast);
}
//if (icat == 1 && catnames.size() == 2) continue; // only 1 cat, so don't need combination
for (int i=1; i<(plot[icat]->GetXaxis()->GetNbins()+1); ++i) {
// this defines the 'binning' we use for the error bands
double lowedge = plot[icat]->GetXaxis()->GetBinLowEdge(i);
double upedge = plot[icat]->GetXaxis()->GetBinUpEdge(i);
double center = plot[icat]->GetXaxis()->GetBinCenter(i);
// get the nominal value at the center of the bin
double nombkg = nomcurve->interpolate(center);
nlim->setVal(nombkg);
hmass->setRange("errRange",lowedge,upedge);
// this is the new extended PDF whith the normalization restricted to the bin-area
RooAbsPdf *extLimPdf = NULL;
if( iscombcat ) {
extLimPdf = new RooSimultaneous("epdf","",finalcat);
// loop over the cats and generate temporary extended PDFs
for (int jcat=0; jcat<(catnames.size()-1); ++jcat) {
RooRealVar *rvar = dynamic_cast<RooRealVar*>(normu_vec.at(jcat));
if (rvar) rvar->setVal(fitcurves.at(jcat)->interpolate(center));
RooExtendPdf *ecpdf = new RooExtendPdf(TString::Format("ecpdf%s",catnames.at(jcat).Data()),"",*pdfShape_vec.at(jcat),*normu_vec.at(jcat),"errRange");
static_cast<RooSimultaneous*>(extLimPdf)->addPdf(*ecpdf,catnames.at(jcat));
}
} else
extLimPdf = new RooExtendPdf("extLimPdf","",*thisPdf,*nlim,"errRange");
RooAbsReal *nll = extLimPdf->createNLL(*datanorm,Extended(),NumCPU(1));
RooMinimizer minim(*nll);
minim.setStrategy(0);
double clone = 1.0 - 2.0*RooStats::SignificanceToPValue(1.0);
double cltwo = 1.0 - 2.0*RooStats::SignificanceToPValue(2.0);
if (iscombcat) minim.setStrategy(2);
minim.migrad();
if (!iscombcat) {
minim.minos(*nlim);
}
else {
minim.hesse();
nlim->removeAsymError();
}
if( verbose )
printf("errlo = %5f, errhi = %5f\n",nlim->getErrorLo(),nlim->getErrorHi());
onesigma->SetPoint(i-1,center,nombkg);
onesigma->SetPointError(i-1,0.,0.,-nlim->getErrorLo(),nlim->getErrorHi());
// to get the 2-sigma bands...
minim.setErrorLevel(0.5*pow(ROOT::Math::normal_quantile(1-0.5*(1-cltwo),1.0), 2)); // the 0.5 is because qmu is -2*NLL
// eventually if cl = 0.95 this is the usual 1.92!
if (!iscombcat) {
minim.migrad();
minim.minos(*nlim);
}
else {
nlim->setError(2.0*nlim->getError());
nlim->removeAsymError();
}
twosigma->SetPoint(i-1,center,nombkg);
twosigma->SetPointError(i-1,0.,0.,-nlim->getErrorLo(),nlim->getErrorHi());
// for memory clean-up
delete nll;
delete extLimPdf;
}
hmass->setRange("errRange",massmin,massmax);
if( verbose )
onesigma->Print("V");
// plot[icat] the error bands
twosigma->SetLineColor(kGreen);
twosigma->SetFillColor(kGreen);
twosigma->SetMarkerColor(kGreen);
twosigma->Draw("L3 SAME");
onesigma->SetLineColor(kYellow);
onesigma->SetFillColor(kYellow);
onesigma->SetMarkerColor(kYellow);
onesigma->Draw("L3 SAME");
plot[icat]->Draw("SAME");
// and add the error bands to the legend
legmc->AddEntry(onesigma,"#pm1 #sigma","F");
legmc->AddEntry(twosigma,"#pm2 #sigma","F");
}
std::cout<<" trying ***2 to plot #"<<icat<<std::endl;
// rest of the legend ....
legmc->SetBorderSize(0);
legmc->SetFillStyle(0);
legmc->Draw();
lat[icat] = new TLatex(103.0,0.9*plot[icat]->GetMaximum(),TString::Format("#scale[0.7]{#splitline{CMS preliminary}{#sqrt{s} = %.1f TeV L = %.2f fb^{-1}}}",theCMenergy,intLumi->getVal()));
lat2[icat] = new TLatex(103.0,0.75*plot[icat]->GetMaximum(),catdesc.at(icat));
lat[icat] ->Draw();
lat2[icat]->Draw();
// -------------------------------------------------------
// save canvas in different formats
canbkg[icat]->SaveAs(TString("databkg") + catname + TString(".pdf"));
canbkg[icat]->SaveAs(TString("databkg") + catname + TString(".eps"));
canbkg[icat]->SaveAs(TString("databkg") + catname + TString(".root"));
}
return;
}
TH1D* runSig(RooWorkspace* ws,
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData",
const char* asimov1DataName = "asimovData_1",
const char* conditional1Snapshot = "conditionalGlobs_1",
const char* nominalSnapshot = "nominalGlobs")
{
string defaultMinimizer = "Minuit"; // or "Minuit"
int defaultStrategy = 2; // Minimization strategy. 0-2. 0 = fastest, least robust. 2 = slowest, most robust
double mu_profile_value = 1; // mu value to profile the obs data at wbefore generating the expected
bool doUncap = 1; // uncap p0
bool doInj = 0; // setup the poi for injection study (zero is faster if you're not)
bool doMedian = 1; // compute median significance
bool isBlind = 0; // Dont look at observed data
bool doConditional = !isBlind; // do conditional expected data
bool doObs = !isBlind; // compute observed significance
TStopwatch timer;
timer.Start();
if (!ws)
{
cout << "ERROR::Workspace is NULL!" << endl;
return NULL;
}
ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName);
if (!mc)
{
cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl;
return NULL;
}
RooDataSet* data = (RooDataSet*)ws->data(dataName);
if (!data)
{
cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl;
return NULL;
}
mc->GetNuisanceParameters()->Print("v");
//RooNLLVar::SetIgnoreZeroEntries(1);
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(defaultMinimizer.c_str());
ROOT::Math::MinimizerOptions::SetDefaultStrategy(defaultStrategy);
ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(-1);
// cout << "Setting max function calls" << endl;
//ROOT::Math::MinimizerOptions::SetDefaultMaxFunctionCalls(20000);
//RooMinimizer::SetMaxFunctionCalls(10000);
ws->loadSnapshot("conditionalNuis_0");
RooArgSet nuis(*mc->GetNuisanceParameters());
RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first();
RooAbsPdf* pdf_temp = mc->GetPdf();
string condSnapshot(conditional1Snapshot);
RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters();
RooNLLVar* obs_nll = doObs ? (RooNLLVar*)pdf_temp->createNLL(*data, Constrain(nuis_tmp2)) : NULL;
RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName);
if (!asimovData1)
{
cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
string mu_str, mu_prof_str;
asimovData1 = makeAsimovData(mc, doConditional, ws, obs_nll, 1, &mu_str, &mu_prof_str, mu_profile_value, true);
condSnapshot="conditionalGlobs"+mu_prof_str;
//makeAsimovData(mc, true, ws, mc->GetPdf(), data, 0);
//ws->Print();
//asimovData1 = (RooDataSet*)ws->data("asimovData_1");
}
if (!doUncap) mu->setRange(0, 40);
else mu->setRange(-40, 40);
RooAbsPdf* pdf = mc->GetPdf();
RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters();
RooNLLVar* asimov_nll = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp1));
//do asimov
mu->setVal(1);
mu->setConstant(0);
if (!doInj) mu->setConstant(1);
int status,sign;
double med_sig=0,obs_sig=0,asimov_q0=0,obs_q0=0;
if (doMedian)
{
ws->loadSnapshot(condSnapshot.c_str());
if (doInj) ws->loadSnapshot("conditionalNuis_inj");
else ws->loadSnapshot("conditionalNuis_1");
mc->GetGlobalObservables()->Print("v");
mu->setVal(0);
mu->setConstant(1);
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov_nll_cond = asimov_nll->getVal();
mu->setVal(1);
if (doInj) ws->loadSnapshot("conditionalNuis_inj");
else ws->loadSnapshot("conditionalNuis_1");
if (doInj) mu->setConstant(0);
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov_nll_min = asimov_nll->getVal();
asimov_q0 = 2*(asimov_nll_cond - asimov_nll_min);
if (doUncap && mu->getVal() < 0) asimov_q0 = -asimov_q0;
sign = int(asimov_q0 != 0 ? asimov_q0/fabs(asimov_q0) : 0);
med_sig = sign*sqrt(fabs(asimov_q0));
ws->loadSnapshot(nominalSnapshot);
}
if (doObs)
{
ws->loadSnapshot("conditionalNuis_0");
mu->setVal(0);
mu->setConstant(1);
status = minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll_cond = obs_nll->getVal();
//ws->loadSnapshot("ucmles");
mu->setConstant(0);
status = minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll_min = obs_nll->getVal();
obs_q0 = 2*(obs_nll_cond - obs_nll_min);
if (doUncap && mu->getVal() < 0) obs_q0 = -obs_q0;
sign = int(obs_q0 == 0 ? 0 : obs_q0 / fabs(obs_q0));
if (!doUncap && ((obs_q0 < 0 && obs_q0 > -0.1) || mu->getVal() < 0.001)) obs_sig = 0;
else obs_sig = sign*sqrt(fabs(obs_q0));
}
cout << "obs: " << obs_sig << endl;
cout << "Observed significance: " << obs_sig << endl;
if (med_sig)
{
cout << "Median test stat val: " << asimov_q0 << endl;
cout << "Median significance: " << med_sig << endl;
}
TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2);
h_hypo->SetBinContent(1, obs_sig);
h_hypo->SetBinContent(2, med_sig);
timer.Stop();
timer.Print();
return h_hypo;
}
void cutChecker()
{
int kCut =2 ;//1:vProb, 2:dca, 3:MatchedStations, 4:ctau/ctauErr
int pbpb=false;
gROOT->Macro("./cm/logon.C+");//it all looks much nicer with this.
// TString fname2011="../dimuonTree_HI2011_fulldataset_trkRot.root";
// TFile *_file0 = TFile::Open(fname2011);
// TTree *upsi2011 = (TTree*)_file0->Get("UpsilonTree");
if(pbpb) { TString fname2013=" ../dimuonTree_upsiMiniTree_AA2p76tev_WithIDCuts_RunHIN-15-001_trigBit1_allTriggers0.root";//../dimuonTree_upsiMiniTree_aa276tev_regitreco_glbglb_Runa_trigBit1_allTriggers0_pt4.root";
}else if(!pbpb){
TString fname2013=" ../dimuonTree_upsiMiniTree_pp2p76tev_noIDVars_GlbGlb_RunHIN-15-001_trigBit2_allTriggers0.root";
}
//TString fname2013="../upsiMiniTree_pyquen1S_noMuonPtCuts_QQtrigbit1_Trig_analysisOK_20140729_cuts10-006.root";
TFile *_file1 = TFile::Open(fname2013);
TTree *upsi2013 = (TTree*)_file1->Get("UpsilonTree");
RooRealVar* upsPt = new RooRealVar("upsPt","p_{T}(#Upsilon)",0,60,"GeV");
// RooRealVar* upsEta = new RooRealVar("upsEta", "upsEta" ,-10,10);
RooRealVar* upsRapidity= new RooRealVar("upsRapidity", "upsRapidity",-1000, 1000);
RooRealVar* vProb = new RooRealVar("vProb", "vProb" ,0.01,1.00);
RooRealVar* _dca = new RooRealVar("_dca", "_dca" ,0.0,5.00);
RooRealVar* _ctau = new RooRealVar("_ctau", "_ctau" ,-100,100,"cm");
RooRealVar* _ctauErr = new RooRealVar("_ctauErr", "_ctauErr" ,-100,100,"cm");
RooRealVar* muPlusPt = new RooRealVar("muPlusPt","muPlusPt",3.5,100);
RooRealVar* muMinusPt = new RooRealVar("muMinusPt","muMinusPt",3.5,100);
RooRealVar* muPlusEta = new RooRealVar("muPlusEta","muPlusEta", -2.4,2.4);
RooRealVar* muMinusEta = new RooRealVar("muMinusEta","muMinusEta",-2.4,2.4);
RooRealVar* _mupl_StationsMatched = new RooRealVar("_mupl_StationsMatched","_mupl_StationsMatched",0,5);
RooRealVar* _mumi_StationsMatched = new RooRealVar("_mumi_StationsMatched","_mumi_StationsMatched",0,5);
RooRealVar* muMinusEta = new RooRealVar("muMinusEta","muMinusEta",-2.4,2.4);
RooRealVar* mass = new RooRealVar("invariantMass","#mu#mu mass",7,14,"GeV/c^{2}");
TCut cut_acc = " ((muPlusPt >3.5 && muMinusPt>4)||(muPlusPt>4 &&muMinusPt>3.5)) && abs(upsRapidity)<2.4 && (invariantMass<14 && invariantMass>7)"; //
cout << "cut: "<< cut_acc.Print() << endl;
// TCut cut_add(cutList(1,1));
//cout << "cut: "<< cut_add.Print() << endl;
switch (kCut){
case 1: //vProb]
RooDataSet *data0 = new RooDataSet("data0","data0",upsi2013,
RooArgSet(*mass,*muPlusPt,*muMinusPt,*upsRapidity,*vProb));
string cut[4]={"vProb>0.01",// very loose
"vProb>0.05",
"vProb>0.1",
"vProb>0.2"};//very tight
// for plotting purposes...
break;
case 2: //dca
RooDataSet *data0 = new RooDataSet("data0","data0",upsi2013,
RooArgSet(*mass,*muPlusPt,*muMinusPt,*upsRapidity,*_dca));
string cut[4]={"_dca<0.004", //very tight
"_dca<0.006",
"_dca<0.008",
"_dca<0.01"}; // very loose
break;
case 3: // number of matched Stations
RooDataSet *data0 = new RooDataSet("data0","data0",upsi2013,
RooArgSet(*mass,*muPlusPt,*muMinusPt,*upsRapidity,*_mupl_StationsMatched,*_mumi_StationsMatched));
string cut[4]={ "_mumi_StationsMatched>0&&_mupl_StationsMatched>0",
"_mumi_StationsMatched>1&&_mupl_StationsMatched>1",
"_mumi_StationsMatched>2&&_mupl_StationsMatched>2",
"_mumi_StationsMatched>3&&_mupl_StationsMatched>3"};
string cutname[4]={ "at least one",
"more than 1",
"more than 2",
"more than 3"};
break;
case 4: ///fabs(ctau/ctau_err)
RooDataSet *data0 = new RooDataSet("data0","data0",upsi2013,
RooArgSet(*mass,*muPlusPt,*muMinusPt,*upsRapidity,*_ctau,*_ctauErr));
string cut[4]={"abs(_ctau/_ctauErr)<5",//very loose
"abs(_ctau/_ctauErr)<4" ,
"abs(_ctau/_ctauErr)<3" ,
"abs(_ctau/_ctauErr)<2" }; //tighter
string cutname[4]={"|c#tau/#sigma(c#tau)| < 5",
"|c#tau/#sigma(c#tau)| < 4",
"|c#tau/#sigma(c#tau)| < 3",
"|c#tau/#sigma(c#tau)| < 2"};
break;
default:
cout<< "no Cut Variable specified!"<<endl; break;
}
TCut cut_add1((cut[0]).c_str());
TCut cut_add2((cut[1]).c_str());
TCut cut_add3((cut[2]).c_str());
TCut cut_add4((cut[3]).c_str());
TString figName_(Form("%s",(cut[0]).c_str()));
figName_.ReplaceAll(">","_gt");
figName_.ReplaceAll("<","_lt");
figName_.ReplaceAll(".","p");
figName_.ReplaceAll("&&","_AND_");
figName_.ReplaceAll("||","_OR_");
figName_.ReplaceAll("(","");
figName_.ReplaceAll("/","-");
figName_.ReplaceAll(")","");
cout << "hello"<< endl;
// cut_add.Print();
int nt = data0->sumEntries();
redData1 = ( RooDataSet*) data0->reduce(Cut(cut_acc+cut_add1));
redData1->Print();
TH1D *MReco1;
MReco1 = new TH1D("MReco1","Reco di-muon mass",70,7,14);
MReco1 = (TH1D*) redData1->createHistogram("invariantMass",*mass);
redData2 = ( RooDataSet*) data0->reduce(Cut(cut_acc+cut_add2));
redData2->Print();
TH1D *MReco2;
MReco2 = new TH1D("MReco2","Reco di-muon mass",70,7,14);
MReco2 = (TH1D*) redData2->createHistogram("invariantMass",*mass);
redData3 = ( RooDataSet*) data0->reduce(Cut(cut_acc+cut_add3));
redData3->Print();
TH1D *MReco3;
MReco3 = new TH1D("MReco3","Reco di-muon mass",70,7,14);
MReco3 = (TH1D*) redData3->createHistogram("invariantMass",*mass);
redData4 = ( RooDataSet*) data0->reduce(Cut(cut_acc+cut_add4));
redData4->Print();
TH1D *MReco4;
MReco4 = new TH1D("MReco4","Reco di-muon mass",70,7,14);
MReco4 = (TH1D*) redData4->createHistogram("invariantMass",*mass);
const double M1S = 9.46; //upsilon 1S pgd mass value
const double M2S = 10.023; //upsilon 2S pgd mass value
const double M3S = 10.355; //upsilon 3S pgd mass value
RooRealVar *nsig1f = new RooRealVar("N_{#Upsilon(1S)}","nsig1S",0,nt*10);
RooRealVar *nsig2f = new RooRealVar("N_{#Upsilon(2S)}","nsig2S", nt*0.25,-1*nt,10*nt);
RooRealVar *nsig3f = new RooRealVar("N_{#Upsilon(3S)}","nsig3S", nt*0.25,-1*nt,10*nt);
RooRealVar *mean = new RooRealVar("mass1S","#Upsilon mean",M1S,M1S-0.1,M1S+0.1);
RooConstVar *rat2 = new RooConstVar("rat2", "rat2", M2S/M1S);
RooConstVar *rat3 = new RooConstVar("rat3", "rat3", M3S/M1S);
// scale mean and resolution by mass ratio
RooFormulaVar *mean1S = new RooFormulaVar("mean1S","@0",RooArgList(*mean));
RooFormulaVar *mean2S = new RooFormulaVar("mean2S","@0*@1", RooArgList(*mean,*rat2));
RooFormulaVar *mean3S = new RooFormulaVar("mean3S","@0*@1", RooArgList(*mean,*rat3));
//detector resolution ?? where is this coming from?
RooRealVar *sigma1 = new RooRealVar("#sigma_{CB1}","#sigma_{CB1}",0,0.5); //
RooFormulaVar *sigma1S = new RooFormulaVar("sigma1S","@0" ,RooArgList(*sigma1));
RooFormulaVar *sigma2S = new RooFormulaVar("sigma2S","@0*@1",RooArgList(*sigma1,*rat2));
RooFormulaVar *sigma3S = new RooFormulaVar("sigma3S","@0*@1",RooArgList(*sigma1,*rat3));
RooRealVar *alpha = new RooRealVar("#alpha_{CB}","tail shift",0.01,8); // MC 5tev 1S pol2
RooRealVar *npow = new RooRealVar("npow","power order",1,60); // MC 5tev 1S pol2
RooRealVar *sigmaFraction = new RooRealVar("sigmaFraction","Sigma Fraction",0.,1.);
// scale the sigmaGaus with sigma1S*scale=sigmaGaus now.
RooRealVar *scaleWidth = new RooRealVar("#sigma_{CB2}/#sigma_{CB1}","scaleWidth",1.,2.7);
RooFormulaVar *sigmaGaus = new RooFormulaVar("sigmaGaus","@0*@1", RooArgList(*sigma1,*scaleWidth));
RooFormulaVar *sigmaGaus2 = new RooFormulaVar("sigmaGaus","@0*@1*@2", RooArgList(*sigma1,*scaleWidth,*rat2));
RooFormulaVar *sigmaGaus3 = new RooFormulaVar("sigmaGaus","@0*@1*@2", RooArgList(*sigma1,*scaleWidth,*rat3));
RooCBShape *cb1S_1 = new RooCBShape ("cb1S_1", "FSR cb 1s",
*mass,*mean1S,*sigma1,*alpha,*npow);
RooCBShape *cb1S_2 = new RooCBShape ("cb1S_2", "FSR cb 1s",
*mass,*mean1S,*sigmaGaus,*alpha,*npow);
RooAddPdf *sig1S = new RooAddPdf ("cbcb","1S mass pdf",
RooArgList(*cb1S_1,*cb1S_2),*sigmaFraction);
// /// Upsilon 2S
RooCBShape *cb2S_1 = new RooCBShape ("cb2S_1", "FSR cb 2s",
*mass,*mean2S,*sigma2S,*alpha,*npow);
RooCBShape *cb2S_2 = new RooCBShape ("cb2S_2", "FSR cb 2s",
*mass,*mean2S,*sigmaGaus2,*alpha,*npow);
RooAddPdf *sig2S = new RooAddPdf ("sig2S","2S mass pdf",
RooArgList(*cb2S_1,*cb2S_2),*sigmaFraction);
// /// Upsilon 3S
RooCBShape *cb3S_1 = new RooCBShape ("cb3S_1", "FSR cb 3s",
*mass,*mean3S,*sigma3S,*alpha,*npow);
RooCBShape *cb3S_2 = new RooCBShape ("cb3S_2", "FSR cb 3s",
*mass,*mean3S,*sigmaGaus3,*alpha,*npow);
RooAddPdf *sig3S = new RooAddPdf ("sig3S","3S mass pdf",
RooArgList(*cb3S_1,*cb3S_2),*sigmaFraction);
// bkg Chebychev
RooRealVar *nbkgd = new RooRealVar("n_{Bkgd}","nbkgd",0,nt);
RooRealVar *bkg_a1 = new RooRealVar("a1_bkg", "bkg_{a1}", 0, -5, 5);
RooRealVar *bkg_a2 = new RooRealVar("a2_Bkg", "bkg_{a2}", 0, -5, 5);
RooRealVar *bkg_a3 = new RooRealVar("a3_Bkg", "bkg_{a3}", 0, -2, 2);
RooAbsPdf *pdf_combinedbkgd = new RooChebychev("bkgPdf","bkgPdf",
*mass, RooArgList(*bkg_a1,*bkg_a2));
RooRealVar turnOn("turnOn","turnOn",2.,8.6);
RooRealVar width("width","width",0.3,8.5);// MB 2.63
RooRealVar decay("decay","decay",1,18);// MB: 3.39
RooGenericPdf *ErrPdf = new RooGenericPdf("ErrPdf","ErrPdf",
"exp(-@0/decay)*(TMath::Erf((@0-turnOn)/width)+1)",
RooArgList(*mass,turnOn,width,decay));
// bkg_a2->setVal(0);
// bkg_a2->setConstant();
RooDataHist binnedData1 ("binnedData1","binnedData1",*mass,Import(*MReco1));
RooDataHist binnedData2 ("binnedData2","binnedData2",*mass,Import(*MReco2));
RooDataHist binnedData3 ("binnedData3","binnedData3",*mass,Import(*MReco3));
RooDataHist binnedData4 ("binnedData4","binnedData4",*mass,Import(*MReco4));
RooAbsPdf *pdf = new RooAddPdf ("pdf","total p.d.f.",
RooArgList(*sig1S,*sig2S,*sig3S,*ErrPdf),
RooArgList(*nsig1f,*nsig2f,*nsig3f,*nbkgd));
npow->setVal(2);
npow->setConstant();
//for the plots!
TCanvas c; c.cd();
TPad phead("phead","phead",0.05,0.9,1.,1.,0,0,0);
phead.Draw(); phead.cd();
TLatex *cms = new TLatex (0.1,0.1,"CMS Internal");
cms->SetTextFont(40);
cms->SetTextSize(0.4);
cms->SetTextColor(kBlack);
cms->Draw();
if(pbpb){ TLatex *pbpb = new TLatex (0.6,0.1,"PbPb #sqrt{s_{NN}} = 2.76 TeV");
pbpb->SetTextFont(42);
pbpb->SetTextSize(0.35);
pbpb->SetTextColor(kBlack);
pbpb->Draw();
}else if(!pbpb){
TLatex *pp = new TLatex (0.6,0.1,"pp #sqrt{s} = 2.76 TeV");
pp->SetTextFont(42);
pp->SetTextSize(0.35);
pp->SetTextColor(kBlack);
pp->Draw();
}
TPad pbody("pbody","pbody",0.0,0.0,1.,0.9,0,0,0);
c.cd();
pbody.SetLeftMargin(0.15);
pbody.Draw(); pbody.cd();
RooPlot* frame = mass->frame(Bins(70),Range(7,14));
// 1st round
RooAbsReal* nll1 = pdf->createNLL(binnedData1,NumCPU(4)) ;
RooMinuit(*nll1).migrad();
RooMinuit(*nll1).hesse();
binnedData1.plotOn(frame,Name("theData"),MarkerSize(0.6),MarkerStyle(20),MarkerColor(kBlue));
pdf->plotOn(frame,Name("thePdf"),LineColor(kBlue));
double signif1 = nsig1f->getVal()/nsig1f->getError();
double signif1_2s = nsig2f->getVal()/nsig2f->getError();
double signif1_3s = nsig3f->getVal()/nsig3f->getError();
MReco1->SetMarkerSize(1.0);
MReco1->SetMarkerStyle(20);
MReco1->SetMarkerColor(kBlue);
MReco1->Draw("esame");
// 2nd round
RooAbsReal* nll2 = pdf->createNLL(binnedData2,NumCPU(4)) ;
RooMinuit(*nll2).migrad();
RooMinuit(*nll2).hesse();
binnedData2.plotOn(frame,Name("theData"),MarkerSize(0.6),MarkerStyle(20),MarkerColor(kRed));
pdf->plotOn(frame,Name("thePdf"),LineColor(kRed));
double signif2 = nsig1f->getVal()/nsig1f->getError();
double signif2_2s = nsig2f->getVal()/nsig2f->getError();
double signif2_3s = nsig3f->getVal()/nsig3f->getError();
MReco2->SetMarkerSize(1.0);
MReco2->SetMarkerStyle(20);
MReco2->SetMarkerColor(kRed);
MReco2->Draw("esame");
// 3rd round
RooAbsReal* nll3 = pdf->createNLL(binnedData3,NumCPU(4)) ;
RooMinuit(*nll3).migrad();
RooMinuit(*nll3).hesse();
binnedData3.plotOn(frame,Name("theData"),MarkerSize(0.6),MarkerStyle(20),MarkerColor(8));
pdf->plotOn(frame,Name("thePdf"),LineColor(8));
double signif3 = nsig1f->getVal()/nsig1f->getError();
double signif3_2s = nsig2f->getVal()/nsig2f->getError();
double signif3_3s = nsig3f->getVal()/nsig3f->getError();
MReco3->SetMarkerSize(1.0);
MReco3->SetMarkerStyle(20);
MReco3->SetMarkerColor(8);
MReco3->Draw("esame");
// 4th round
RooAbsReal* nll4 = pdf->createNLL(binnedData4,NumCPU(4)) ;
RooMinuit(*nll4).migrad();
RooMinuit(*nll4).hesse();
binnedData4.plotOn(frame,Name("theData"),MarkerSize(0.6),MarkerStyle(20),MarkerColor(28));
pdf->plotOn(frame,Name("thePdf"),LineColor(28));
double signif4 = nsig1f->getVal()/nsig1f->getError();
double signif4_2s = nsig2f->getVal()/nsig2f->getError();
double signif4_3s = nsig3f->getVal()/nsig3f->getError();
// pdf->paramOn(frame,Layout(0.5,0.95,0.9),Parameters(RooArgSet(signif)),Format("N",AutoPrecision(1)));
MReco4->SetMarkerSize(1.0);
MReco4->SetMarkerStyle(20);
MReco4->SetMarkerColor(28);
MReco4->Draw("esame");
// and all that.
frame->SetTitle("");
frame->GetXaxis()->SetTitle("m_{#mu^{+}#mu^{-}} (GeV/c^{2})");
frame->GetXaxis()->CenterTitle(kTRUE);
frame->GetYaxis()->SetTitleOffset(2);
frame->GetXaxis()->SetTitleOffset(1.5);
frame->Draw();
TLegend *legend = new TLegend(0.5,0.6,0.95,0.9);
legend->SetTextSize(0.034);
legend->SetFillStyle(0);
legend->SetFillColor(0);
legend->SetBorderSize(0);
legend->SetTextFont(42);
legend->AddEntry(MReco1,"1S significance, #Sigma","");
switch (kCut){
case 1: //vProb]
legend->AddEntry(MReco1,"Vertex Probability","");
legend->AddEntry(MReco1,Form("%s, #Sigma = %0.2f",cut[0].c_str(),signif1),"p");
legend->AddEntry(MReco2,Form("%s, #Sigma = %0.2f",cut[1].c_str(),signif2),"p");
legend->AddEntry(MReco3,Form("%s, #Sigma = %0.2f",cut[2].c_str(),signif3),"p");
legend->AddEntry(MReco4,Form("%s, #Sigma = %0.2f",cut[3].c_str(),signif4),"p");
break;
case 2:
legend->AddEntry(MReco1,"Dist. of closest approach","");
legend->AddEntry(MReco1,Form("%s, #Sigma = %0.2f",cut[0].c_str(),signif1),"p");
legend->AddEntry(MReco2,Form("%s, #Sigma = %0.2f",cut[1].c_str(),signif2),"p");
legend->AddEntry(MReco3,Form("%s, #Sigma = %0.2f",cut[2].c_str(),signif3),"p");
legend->AddEntry(MReco4,Form("%s, #Sigma = %0.2f",cut[3].c_str(),signif4),"p");
break;
case 3:
legend->AddEntry(MReco1,"Stations matched to each track","");
legend->AddEntry(MReco1,Form("%s, #Sigma = %0.2f",cutname[0].c_str(),signif1),"p");
legend->AddEntry(MReco2,Form("%s, #Sigma = %0.2f",cutname[1].c_str(),signif2),"p");
legend->AddEntry(MReco3,Form("%s, #Sigma = %0.2f",cutname[2].c_str(),signif3),"p");
legend->AddEntry(MReco4,Form("%s, #Sigma = %0.2f",cutname[3].c_str(),signif4),"p");
break;
case 4:
legend->AddEntry(MReco1,"Pseudo-proper decay length","");
legend->AddEntry(MReco1,Form("%s, #Sigma = %0.2f",cutname[0].c_str(),signif1),"p");
legend->AddEntry(MReco2,Form("%s, #Sigma = %0.2f",cutname[1].c_str(),signif2),"p");
legend->AddEntry(MReco3,Form("%s, #Sigma = %0.2f",cutname[2].c_str(),signif3),"p");
legend->AddEntry(MReco4,Form("%s, #Sigma = %0.2f",cutname[3].c_str(),signif4),"p");
break;
default: break;
}
legend->Draw();
// legend->AddEntry(MReco1,Form(",),"f");
// TLatex latex1;
// latex1.SetNDC();
// latex1.SetTextSize(0.032);
// latex1.DrawLatex(0.35,1.-0.05*2.,Form("significance: #Sigma vs %s",cut[0].c_str()));
// latex1.DrawLatex(0.55,1.-0.05*3.,Form(" #Sigma = %f",signif1));
// latex1.DrawLatex(0.55,1.-0.05*4.,Form(" #Sigma = %f",signif2));
// latex1.DrawLatex(0.55,1.-0.05*5.,Form(" #Sigma = %f",signif3));
// latex1.DrawLatex(0.55,1.-0.05*6.,Form(" #Sigma = %f",signif4));
c.Draw();
if(pbpb){
c.SaveAs("~/Desktop/Grenelle/"+figName_+".pdf");
}
else if(!pbpb){
c.SaveAs("~/Desktop/Grenelle/"+figName_+"_pp.pdf");
}
cout <<" SIGNIFICANCES \\Sigma OF ALL STATES:" << endl;
cout << "xxxx - \\Sigma(1S) \& \\Sigma(2S) \& \\Sigma(3S) " <<endl;
cout << cut[0].c_str() <<" & "<< signif1 << " &"<< signif1_2s << " & "<< signif1_3s << endl;
cout << cut[1].c_str() <<" & "<< signif2 << " &"<< signif2_2s << " & "<< signif2_3s << endl;
cout << cut[2].c_str() <<" & "<< signif3 << " &"<< signif3_2s << " & "<< signif3_3s << endl;
cout << cut[3].c_str() <<" & "<< signif4 << " &"<< signif4_2s << " & "<< signif4_3s << endl;
}
void eregtest_flextest(bool dobarrel, bool doele) {
TString dirname = "/afs/cern.ch/work/b/bendavid/bare/eregtestoutalphafix2_float/";
gSystem->mkdir(dirname,true);
gSystem->cd(dirname);
TString fname;
if (doele && dobarrel)
fname = "wereg_ele_eb.root";
else if (doele && !dobarrel)
fname = "wereg_ele_ee.root";
else if (!doele && dobarrel)
fname = "wereg_ph_eb.root";
else if (!doele && !dobarrel)
fname = "wereg_ph_ee.root";
//TString infile = TString::Format("/afs/cern.ch/work/b/bendavid/bare/eregAug10RCalphafixphiblind//%s",fname.Data());
TString infile = TString::Format("/data/bendavid/regflextesting/%s",fname.Data());
TFile *fws = TFile::Open(infile);
RooWorkspace *ws = (RooWorkspace*)fws->Get("wereg");
//RooGBRFunction *func = static_cast<RooGBRFunction*>(ws->arg("func"));
RooGBRTargetFlex *sigmeant = (RooGBRTargetFlex*)ws->function("sigmeant");
RooRealVar *tgtvar = ws->var("tgtvar");
//tgtvar->removeRange();
//tgtvar->setRange(0.98,1.02);
RooRealVar *rawptvar = new RooRealVar("rawptvar","ph.scrawe/cosh(ph.eta)",1.);
if (!dobarrel) rawptvar->SetTitle("(ph.scrawe+ph.scpse)/cosh(ph.eta)");
RooRealVar *rawevar = new RooRealVar("rawevar","ph.scrawe",1.);
if (!dobarrel) rawevar->SetTitle("(ph.scrawe+ph.scpse)");
RooRealVar *nomevar = new RooRealVar("nomevar","ph.e",1.);
RooArgList vars;
vars.add(sigmeant->FuncVars());
vars.add(*tgtvar);
vars.add(*rawptvar);
vars.add(*rawevar);
vars.add(*nomevar);
RooArgList condvars;
condvars.add(sigmeant->FuncVars());
RooRealVar weightvar("weightvar","",1.);
TTree *dtree;
if (doele) {
TFile *fdin = TFile::Open("/data/bendavid/regTreesAug1/hgg-2013Final8TeV_reg_s12-zllm50-v7n_noskim.root");
TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterSingleInvert");
dtree = (TTree*)ddir->Get("hPhotonTreeSingle");
}
else {
TFile *fdin = TFile::Open("/data/bendavid/idTreesAug1/hgg-2013Final8TeV_ID_s12-h124gg-gf-v7n_noskim.root");
//TFile *fdin = TFile::Open("/data/bendavid/idTrees_7TeV_Sept17/hgg-2013Final7TeV_ID_s11-h125gg-gf-lv3_noskim.root");
TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterPreselNoSmear");
dtree = (TTree*)ddir->Get("hPhotonTreeSingle");
}
// if (0)
// {
//
// TFile *fdin = TFile::Open("/data/bendavid/8TeVFinalTreesSept17/hgg-2013Final8TeV_s12-diphoj-v7n_noskim.root");
// TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterPresel");
// dtree = (TTree*)ddir->Get("hPhotonTreeSingle");
//
// }
if (0)
{
TFile *fdin = TFile::Open("/data/bendavid/diphoTrees8TeVOct6/hgg-2013Final8TeV_s12-h123gg-gf-v7n_noskim.root");
TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterPreselNoSmear");
dtree = (TTree*)ddir->Get("hPhotonTreeSingle");
}
// //TFile *fdin = TFile::Open("/home/mingyang/cms/hist/hgg-2013Moriond/merged/hgg-2013Moriond_s12-diphoj-3-v7a_noskim.root");
// //TFile *fdin = TFile::Open("root://eoscms.cern.ch//eos/cms/store/cmst3/user/bendavid/trainingtreesJul1/hgg-2013Final8TeV_s12-zllm50-v7n_noskim.root");
// TFile *fdin = TFile::Open("root://eoscms.cern.ch///eos/cms/store/cmst3/user/bendavid/idTreesAug1/hgg-2013Final8TeV_ID_s12-h124gg-gf-v7n_noskim.root");
// //TFile *fdin = TFile::Open("root://eoscms.cern.ch//eos/cms/store/cmst3/user/bendavid/regTreesAug1/hgg-2013Final8TeV_reg_s12-zllm50-v7n_noskim.root");
// //TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterSingleInvert");
// TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterPreselNoSmear");
// TTree *dtree = (TTree*)ddir->Get("hPhotonTreeSingle");
/* TFile *fdinsig = TFile::Open("/home/mingyang/cms/hist/hgg-2013Moriond/merged/hgg-2013Moriond_s12-h125gg-gf-v7a_noskim.root");
TDirectory *ddirsig = (TDirectory*)fdinsig->FindObjectAny("PhotonTreeWriterPreselNoSmear");
TTree *dtreesig = (TTree*)ddirsig->Get("hPhotonTreeSingle"); */
TCut selcut;
if (dobarrel) {
selcut = "ph.pt>25. && ph.isbarrel && ph.ispromptgen && abs(ph.sceta)>(-1.0)";
//selcut = "ph.pt>25. && ph.isbarrel && ph.ispromptgen && abs(ph.sceta)>(-1.0) && run==194533 && lumi==5 && evt==1400";
}
else {
selcut = "ph.pt>25 && !ph.isbarrel && ph.ispromptgen";
//selcut = "ph.pt>25 && !ph.isbarrel && ph.ispromptgen && run==194533 && lumi==5 && evt==1400";
}
// TCut selcut = "ph.pt>25. && ph.isbarrel && ph.ispromptgen && abs(ph.sceta)<1.0";
//TCut selcut = "ph.pt>25. && ph.isbarrel && (ph.scrawe/ph.gene)>0. && (ph.scrawe/ph.gene)<2. && ph.ispromptgen";
//TCut selcut = "ph.pt>25. && ph.isbarrel && (ph.gene/ph.scrawe)>0. && (ph.gene/ph.scrawe)<2.";
TCut selweight = "xsecweight(procidx)*puweight(numPU,procidx)";
TCut prescale10 = "(evt%10==0)";
TCut prescale10alt = "(evt%10==1)";
TCut prescale25 = "(evt%25==0)";
TCut prescale100 = "(evt%100==0)";
TCut prescale1000 = "(evt%1000==0)";
TCut evenevents = "(evt%2==0)";
TCut oddevents = "(evt%2==1)";
TCut prescale100alt = "(evt%100==1)";
TCut prescale1000alt = "(evt%1000==1)";
TCut prescale50alt = "(evt%50==1)";
//TCut oddevents = prescale100;
if (doele)
weightvar.SetTitle(prescale100alt*selcut);
else
weightvar.SetTitle(selcut);
RooDataSet *hdata = RooTreeConvert::CreateDataSet("hdata",dtree,vars,weightvar);
// for (int iev=0; iev<hdata->numEntries(); ++iev) {
// const RooArgSet *dset = hdata->get(iev);
//
// condvars = *dset;
// condvars.Print("V");
//
// }
//return;
// if (doele)
// weightvar.SetTitle(prescale100alt*selcut);
// else
// weightvar.SetTitle(selcut);
//RooDataSet *hdatasmall = RooTreeConvert::CreateDataSet("hdatasmall",dtree,vars,weightvar);
// const HybridGBRForestD *forest = func->Forest();
// for (unsigned int itgt=0; itgt<forest->Trees().size(); ++itgt) {
// int ntrees = 0;
// for (unsigned int itree = 0; itree<forest->Trees().at(itgt).size(); ++itree) {
// if (forest->Trees()[itgt][itree].Responses().size()>1) ++ntrees;
// }
// printf("itgt = %i, ntrees = %i\n", int(itgt),ntrees);
// }
RooAbsPdf *sigpdf = ws->pdf("sigpdf");
RooRealVar *scetavar = ws->var("var_1");
RooAbsReal *sigmeanlim = ws->function("sigmeanlim");
RooAbsReal *sigwidthlim = ws->function("sigwidthlim");
RooAbsReal *signlim = ws->function("signlim");
RooAbsReal *sign2lim = ws->function("sign2lim");
RooAbsReal *alphalim = ws->function("sigalphalim");
RooAbsReal *alpha2lim = ws->function("sigalpha2lim");
//RooFormulaVar ecor("ecor","","1./(@0*@1)",RooArgList(*tgtvar,*sigmeanlim));
RooFormulaVar ecor("ecor","","@1/@0",RooArgList(*tgtvar,*sigmeanlim));
//RooFormulaVar ecor("ecor","","@0/@1",RooArgList(*tgtvar,*sigmeanlim));
//RooFormulaVar ecor("ecor","","exp(@1-@0)",RooArgList(*tgtvar,*sigmeanlim));
RooAbsReal *condnll = sigpdf->createNLL(*hdata,ConditionalObservables(sigmeant->FuncVars()));
double condnllval = condnll->getVal();
//RooFormulaVar ecor("ecor","","@1/@0",RooArgList(*tgtvar,*sigmeanlim));
//RooFormulaVar ecor("ecor","","@0/@1",RooArgList(*tgtvar,*sigmeanlim));
//RooFormulaVar ecor("ecor","","@0",RooArgList(*tgtvar));
//RooRealVar *ecorvar = (RooRealVar*)hdata->addColumn(ecor);
// ecorvar->setRange(0.,2.);
// ecorvar->setBins(800);
// RooFormulaVar raw("raw","","1./@0",RooArgList(*tgtvar));
// //RooRealVar *rawvar = (RooRealVar*)hdata->addColumn(raw);
// rawvar->setRange(0.,2.);
// rawvar->setBins(800);
/* RooFormulaVar eraw("eraw","","@0",RooArgList(*tgtvar));
RooRealVar *erawvar = (RooRealVar*)hdatasig->addColumn(eraw);
erawvar->setRange(0.,2.);
erawvar->setBins(400); */
//RooFormulaVar ecor("ptcor","","@0/(@1)",RooArgList(*tgtvar,*sigmeanlim));
RooDataSet *hdataclone = new RooDataSet(*hdata,"hdataclone");
RooRealVar *ecorvar = (RooRealVar*)hdataclone->addColumn(ecor);
RooRealVar *meanvar = (RooRealVar*)hdataclone->addColumn(*sigmeanlim);
RooRealVar *widthvar = (RooRealVar*)hdataclone->addColumn(*sigwidthlim);
RooRealVar *nvar = 0;
if (signlim) nvar = (RooRealVar*)hdataclone->addColumn(*signlim);
RooRealVar *n2var = 0;
if (sign2lim) n2var = (RooRealVar*)hdataclone->addColumn(*sign2lim);
RooRealVar *alphavar = 0;;
if (alphalim) alphavar = (RooRealVar*)hdataclone->addColumn(*alphalim);
RooRealVar *alpha2var = 0;
if (alpha2lim) alpha2var = (RooRealVar*)hdataclone->addColumn(*alpha2lim);
RooFormulaVar ecorfull("ecorfull","","@0*@1",RooArgList(*sigmeanlim,*rawevar));
RooRealVar *ecorfullvar = (RooRealVar*)hdataclone->addColumn(ecorfull);
RooFormulaVar ediff("ediff","","(@0 - @1)/@1",RooArgList(*nomevar,ecorfull));
RooRealVar *ediffvar = (RooRealVar*)hdataclone->addColumn(ediff);
RooFormulaVar fullerr("fullerr","","@0*@1",RooArgList(*ecorvar,*sigwidthlim));
RooRealVar *fullerrvar = (RooRealVar*)hdataclone->addColumn(fullerr);
RooFormulaVar relerr("relerr","","@0/@1",RooArgList(*sigwidthlim,*sigmeanlim));
RooRealVar *relerrvar = (RooRealVar*)hdataclone->addColumn(relerr);
ecorvar->setRange(0.,2.);
ecorvar->setBins(800);
RooFormulaVar raw("raw","","1./@0",RooArgList(*tgtvar));
//RooFormulaVar raw("raw","","exp(-@0)",RooArgList(*tgtvar));
RooRealVar *rawvar = (RooRealVar*)hdataclone->addColumn(raw);
rawvar->setRange(0.,2.);
rawvar->setBins(800);
RooNormPdf sigpdfpeaknorm("sigpdfpeaknorm","",*sigpdf,*tgtvar);
RooRealVar *sigpdfpeaknormvar = (RooRealVar*)hdataclone->addColumn(sigpdfpeaknorm);
RooFormulaVar equivsigma("equivsigma","","@0/sqrt(2.0*TMath::Pi())/@1",RooArgList(sigpdfpeaknorm,*sigmeanlim));
RooRealVar *equivsigmavar = (RooRealVar*)hdataclone->addColumn(equivsigma);
// for (int iev=0; iev<hdataclone->numEntries(); ++iev) {
// const RooArgSet *dset = hdataclone->get(iev);
//
// //condvars = *dset;
// //condvars.Print("V");
// dset->Print("V");
// }
//
// return;
//hdataclone = (RooDataSet*)hdataclone->reduce("(rawptvar/sigmeanlim)>45.");
//hdataclone = (RooDataSet*)hdataclone->reduce("relerr>0.1");
// hdataclone = (RooDataSet*)hdataclone->reduce("sigwidthlim>0.017");
// RooLinearVar *tgtscaled = (RooLinearVar*)ws->function("tgtscaled");
//
// TCanvas *ccor = new TCanvas;
// //RooPlot *plot = tgtvar->frame(0.6,1.2,100);
// RooPlot *plotcor = tgtscaled->frame(0.6,2.0,100);
// hdataclone->plotOn(plotcor);
// sigpdf->plotOn(plotcor,ProjWData(*hdataclone));
// plotcor->Draw();
// ccor->SaveAs("CorE.eps");
// ccor->SetLogy();
// plotcor->SetMinimum(0.1);
// ccor->SaveAs("CorElog.eps");
TCanvas *craw = new TCanvas;
//RooPlot *plot = tgtvar->frame(0.6,1.2,100);
RooPlot *plot = tgtvar->frame(0.8,1.4,400);
//RooPlot *plot = tgtvar->frame(0.0,5.,400);
//RooPlot *plot = tgtvar->frame(0.,5.,400);
//RooPlot *plot = tgtvar->frame(-2.0,2.0,200);
hdataclone->plotOn(plot);
sigpdf->plotOn(plot,ProjWData(*hdataclone));
plot->Draw();
craw->SaveAs("RawE.eps");
craw->SetLogy();
plot->SetMinimum(0.1);
craw->SaveAs("RawElog.eps");
/* new TCanvas;
RooPlot *plotsig = tgtvar->frame(0.6,1.2,100);
hdatasig->plotOn(plotsig);
sigpdf.plotOn(plotsig,ProjWData(*hdatasig));
plotsig->Draw(); */
TCanvas *cmean = new TCanvas;
RooPlot *plotmean = meanvar->frame(0.0,5.0,200);
//RooPlot *plotmean = meanvar->frame(0.5,1.5,200);
//RooPlot *plotmean = meanvar->frame(-1.0,1.0,200);
hdataclone->plotOn(plotmean);
plotmean->Draw();
cmean->SaveAs("mean.eps");
cmean->SetLogy();
plotmean->SetMinimum(0.1);
TCanvas *cwidth = new TCanvas;
RooPlot *plotwidth = widthvar->frame(0.,1.0,200);
hdataclone->plotOn(plotwidth);
plotwidth->Draw();
cwidth->SaveAs("width.eps");
cwidth->SetLogy();
plotwidth->SetMinimum(0.1);
if (signlim) {
TCanvas *cn = new TCanvas;
RooPlot *plotn = nvar->frame(0.,20.,200);
hdataclone->plotOn(plotn);
plotn->Draw();
cn->SaveAs("n.eps");
TCanvas *cnwide = new TCanvas;
RooPlot *plotnwide = nvar->frame(0.,2100.,200);
hdataclone->plotOn(plotnwide);
plotnwide->Draw();
cnwide->SaveAs("nwide.eps");
}
if (sign2lim) {
TCanvas *cn2 = new TCanvas;
RooPlot *plotn2 = n2var->frame(0.,20.,200);
hdataclone->plotOn(plotn2);
plotn2->Draw();
cn2->SaveAs("n2.eps");
TCanvas *cn2wide = new TCanvas;
RooPlot *plotn2wide = n2var->frame(0.,2100.,200);
hdataclone->plotOn(plotn2wide);
plotn2wide->Draw();
cn2wide->SaveAs("n2wide.eps");
}
if (alphalim) {
TCanvas *calpha = new TCanvas;
RooPlot *plotalpha = alphavar->frame(0.,6.,200);
hdataclone->plotOn(plotalpha);
plotalpha->Draw();
calpha->SaveAs("alpha.eps");
calpha->SetLogy();
plotalpha->SetMinimum(0.1);
}
if (alpha2lim) {
TCanvas *calpha2 = new TCanvas;
RooPlot *plotalpha2 = alpha2var->frame(0.,6.,200);
hdataclone->plotOn(plotalpha2);
plotalpha2->Draw();
calpha2->SaveAs("alpha2.eps");
}
TCanvas *ceta = new TCanvas;
RooPlot *ploteta = scetavar->frame(-2.6,2.6,200);
hdataclone->plotOn(ploteta);
ploteta->Draw();
ceta->SaveAs("eta.eps");
//TH1 *heold = hdatasigtest->createHistogram("heold",testvar);
//TH1 *heraw = hdata->createHistogram("heraw",*tgtvar,Binning(800,0.,2.));
TH1 *heraw = hdataclone->createHistogram("hraw",*rawvar,Binning(800,0.,2.));
TH1 *hecor = hdataclone->createHistogram("hecor",*ecorvar);
//heold->SetLineColor(kRed);
hecor->SetLineColor(kBlue);
heraw->SetLineColor(kMagenta);
hecor->GetXaxis()->SetRangeUser(0.6,1.2);
//heold->GetXaxis()->SetRangeUser(0.6,1.2);
TCanvas *cresponse = new TCanvas;
hecor->Draw("HIST");
//heold->Draw("HISTSAME");
heraw->Draw("HISTSAME");
cresponse->SaveAs("response.eps");
cresponse->SetLogy();
cresponse->SaveAs("responselog.eps");
TCanvas *cpeakval = new TCanvas;
RooPlot *plotpeak = sigpdfpeaknormvar->frame(0.,10.,100);
hdataclone->plotOn(plotpeak);
plotpeak->Draw();
TCanvas *cequivsigmaval = new TCanvas;
RooPlot *plotequivsigma = equivsigmavar->frame(0.,0.04,100);
hdataclone->plotOn(plotequivsigma);
plotequivsigma->Draw();
TCanvas *cediff = new TCanvas;
RooPlot *plotediff = ediffvar->frame(-0.01,0.01,100);
hdataclone->plotOn(plotediff);
plotediff->Draw();
printf("make fine histogram\n");
TH1 *hecorfine = hdataclone->createHistogram("hecorfine",*ecorvar,Binning(20e3,0.,2.));
printf("calc effsigma\n");
double effsigma = effSigma(hecorfine);
printf("effsigma = %5f\n",effsigma);
printf("condnll = %5f\n",condnllval);
TFile *fhist = new TFile("hist.root","RECREATE");
fhist->WriteTObject(hecor);
fhist->Close();
return;
/* new TCanvas;
RooPlot *ploteold = testvar.frame(0.6,1.2,100);
hdatasigtest->plotOn(ploteold);
ploteold->Draw();
new TCanvas;
RooPlot *plotecor = ecorvar->frame(0.6,1.2,100);
hdatasig->plotOn(plotecor);
plotecor->Draw(); */
TH2 *profhist = (TH2*)hdataclone->createHistogram("relerrvsE",*ecorfullvar,Binning(50,0.,200.), YVar(*relerrvar,Binning(100,0.,0.05)));
new TCanvas;
profhist->Draw("COLZ");
new TCanvas;
profhist->ProfileX()->Draw();
new TCanvas;
profhist->ProfileY()->Draw();
TH2 *profhistequiv = (TH2*)hdataclone->createHistogram("equiverrvsE",*ecorfullvar,Binning(50,0.,200.), YVar(*equivsigmavar,Binning(100,0.,0.05)));
new TCanvas;
profhistequiv->Draw("COLZ");
new TCanvas;
profhistequiv->ProfileX()->Draw();
new TCanvas;
profhistequiv->ProfileY()->Draw();
}