//#include <typeinfo.h>
void addFlatNuisances(std::string fi){
gSystem->Load("libHiggsAnalysisCombinedLimit.so");
TFile *fin = TFile::Open(fi.c_str());
RooWorkspace *wspace = (RooWorkspace*)fin->Get("w_hmumu");
wspace->Print("");
RooStats::ModelConfig *mc = (RooStats::ModelConfig*)wspace->genobj("ModelConfig");
RooArgSet *nuis = (RooArgSet*) mc->GetNuisanceParameters();
std::cout << "Before...." << std::endl;
nuis->Print();
RooRealVar *mgg = (RooRealVar*)wspace->var("mmm");
// Get all of the "flat" nuisances to be added to the nusiances:
RooArgSet pdfs = (RooArgSet) wspace->allVars();
RooAbsReal *pdf;
TIterator *it_pdf = pdfs.createIterator();
while ( (pdf=(RooAbsReal*)it_pdf->Next()) ){
if (!(std::string(pdf->GetName()).find("zmod") != std::string::npos )) {
if (!(std::string(pdf->GetName()).find("__norm") != std::string::npos )) {
continue;
}
}
pdf->Print();
RooArgSet* pdfpars = (RooArgSet*)pdf->getParameters(RooArgSet(*mgg));
pdfpars->Print();
std::string newname_pdf = (std::string("unconst_")+std::string(pdf->GetName()));
wspace->import(*pdf,RooFit::RenameVariable(pdf->GetName(),newname_pdf.c_str()));
pdf->SetName(newname_pdf.c_str());
nuis->add(*pdf);
}
wspace->var("MH")->setVal(125.0);
std::cout << "After..." << std::endl;
nuis->Print();
mc->SetNuisanceParameters(*nuis);
//RooWorkspace *wspace_new = wspace->Clone();
//mc->SetWorkspace(*wspace_new);
//wspace_new->import(*mc,true);
TFile *finew = new TFile((std::string(fin->GetName())+std::string("_unconst.root")).c_str(),"RECREATE");
//wspace_new->SetName("w");
finew->WriteTObject(wspace);
finew->Close();
}
void rf105_funcbinding()
{
// B i n d T M a t h : : E r f C f u n c t i o n
// ---------------------------------------------------
// Bind one-dimensional TMath::Erf function as RooAbsReal function
RooRealVar x("x","x",-3,3) ;
RooAbsReal* errorFunc = bindFunction("erf",TMath::Erf,x) ;
// Print erf definition
errorFunc->Print() ;
// Plot erf on frame
RooPlot* frame1 = x.frame(Title("TMath::Erf bound as RooFit function")) ;
errorFunc->plotOn(frame1) ;
// B i n d R O O T : : M a t h : : b e t a _ p d f C f u n c t i o n
// -----------------------------------------------------------------------
// Bind pdf ROOT::Math::Beta with three variables as RooAbsPdf function
RooRealVar x2("x2","x2",0,0.999) ;
RooRealVar a("a","a",5,0,10) ;
RooRealVar b("b","b",2,0,10) ;
RooAbsPdf* beta = bindPdf("beta",ROOT::Math::beta_pdf,x2,a,b) ;
// Perf beta definition
beta->Print() ;
// Generate some events and fit
RooDataSet* data = beta->generate(x2,10000) ;
beta->fitTo(*data) ;
// Plot data and pdf on frame
RooPlot* frame2 = x2.frame(Title("ROOT::Math::Beta bound as RooFit pdf")) ;
data->plotOn(frame2) ;
beta->plotOn(frame2) ;
// B i n d R O O T T F 1 a s R o o F i t f u n c t i o n
// ---------------------------------------------------------------
// Create a ROOT TF1 function
TF1 *fa1 = new TF1("fa1","sin(x)/x",0,10);
// Create an observable
RooRealVar x3("x3","x3",0.01,20) ;
// Create binding of TF1 object to above observable
RooAbsReal* rfa1 = bindFunction(fa1,x3) ;
// Print rfa1 definition
rfa1->Print() ;
// Make plot frame in observable, plot TF1 binding function
RooPlot* frame3 = x3.frame(Title("TF1 bound as RooFit function")) ;
rfa1->plotOn(frame3) ;
TCanvas* c = new TCanvas("rf105_funcbinding","rf105_funcbinding",1200,400) ;
c->Divide(3) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
}
void rf506_msgservice()
{
// C r e a t e p d f
// --------------------
// Construct gauss(x,m,s)
RooRealVar x("x","x",-10,10) ;
RooRealVar m("m","m",0,-10,10) ;
RooRealVar s("s","s",1,-10,10) ;
RooGaussian gauss("g","g",x,m,s) ;
// Construct poly(x,p0)
RooRealVar p0("p0","p0",0.01,0.,1.) ;
RooPolynomial poly("p","p",x,p0) ;
// Construct model = f*gauss(x) + (1-f)*poly(x)
RooRealVar f("f","f",0.5,0.,1.) ;
RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
RooDataSet* data = model.generate(x,10) ;
// P r i n t c o n f i g u r a t i o n o f m e s s a g e s e r v i c e
// ---------------------------------------------------------------------------
// Print streams configuration
RooMsgService::instance().Print() ;
cout << endl ;
// A d d i n g I n t e g r a t i o n t o p i c t o e x i s t i n g I N F O s t r e a m
// -----------------------------------------------------------------------------------------------
// Print streams configuration
RooMsgService::instance().Print() ;
cout << endl ;
// Add Integration topic to existing INFO stream
RooMsgService::instance().getStream(1).addTopic(Integration) ;
// Construct integral over gauss to demonstrate new message stream
RooAbsReal* igauss = gauss.createIntegral(x) ;
igauss->Print() ;
// Print streams configuration in verbose, which also shows inactive streams
cout << endl ;
RooMsgService::instance().Print() ;
cout << endl ;
// Remove stream
RooMsgService::instance().getStream(1).removeTopic(Integration) ;
// E x a m p l e s o f p d f v a l u e t r a c i n g s t r e a m
// -----------------------------------------------------------------------
// Show DEBUG level message on function tracing, trace RooGaussian only
RooMsgService::instance().addStream(DEBUG,Topic(Tracing),ClassName("RooGaussian")) ;
// Perform a fit to generate some tracing messages
model.fitTo(*data,Verbose(kTRUE)) ;
// Reset message service to default stream configuration
RooMsgService::instance().reset() ;
// Show DEBUG level message on function tracing on all objects, redirect output to file
RooMsgService::instance().addStream(DEBUG,Topic(Tracing),OutputFile("rf506_debug.log")) ;
// Perform a fit to generate some tracing messages
model.fitTo(*data,Verbose(kTRUE)) ;
// Reset message service to default stream configuration
RooMsgService::instance().reset() ;
// E x a m p l e o f a n o t h e r d e b u g g i n g s t r e a m
// ---------------------------------------------------------------------
// Show DEBUG level messages on client/server link state management
RooMsgService::instance().addStream(DEBUG,Topic(LinkStateMgmt)) ;
RooMsgService::instance().Print("v") ;
// Clone composite pdf g to trigger some link state management activity
RooAbsArg* gprime = gauss.cloneTree() ;
gprime->Print() ;
// Reset message service to default stream configuration
RooMsgService::instance().reset() ;
}
int main() {
// Construct gauss(x,m,s)
RooRealVar x("x","x",-10,10) ;
RooRealVar m("m","m",0,-10,10) ;
RooRealVar s("s","s",1,-10,10) ;
RooGaussian gauss("g","g",x,m,s) ;
// Construct poly(x,p0)
RooRealVar p0("p0","p0",0.01,0.,1.) ;
RooPolynomial poly("p","p",x,p0) ;
// Construct model = f*gauss(x) + (1-f)*poly(x)
RooRealVar f("f","f",0.5,0.,1.) ;
RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
RooDataSet* data = model.generate(x,10);
// P r i n t c o n f i g u r a t i o n o f m e s s a g e s e r v i c e
// ---------------------------------------------------------------------------
// Print streams configuration
RooMsgService::instance().Print() ;
cout << endl ;
// A d d i n g I n t e g r a t i o n t o p i c t o e x i s t i n g I N F O s t r e a m
// -----------------------------------------------------------------------------------------------
// Print streams configuration
RooMsgService::instance().Print() ;
cout << endl ;
// Add Integration topic to existing INFO stream
RooMsgService::instance().getStream(1).addTopic(Integration) ;
// Construct integral over gauss to demonstrate new message stream
RooAbsReal* igauss = gauss.createIntegral(x) ;
igauss->Print() ;
// Print streams configuration in verbose, which also shows inactive streams
cout << endl ;
RooMsgService::instance().Print() ;
cout << endl ;
// Remove stream
RooMsgService::instance().getStream(1).removeTopic(Integration) ;
// E x a m p l e s o f p d f v a l u e t r a c i n g s t r e a m
// -----------------------------------------------------------------------
// Show DEBUG level message on function tracing, trace RooGaussian only
RooMsgService::instance().addStream(DEBUG,Topic(Tracing),ClassName("RooGaussian")) ;
// Perform a fit to generate some tracing messages
model.fitTo(*data,Verbose(kTRUE)) ;
TH1 *histo = model.createHistogram("histo");
return 0;
}
void ws_constrained_profile3D( const char* wsfile = "rootfiles/ws-data-unblind.root",
const char* new_poi_name = "n_M234_H4_3b",
int npoiPoints = 20,
double poiMinVal = 0.,
double poiMaxVal = 20.,
double constraintWidth = 1.5,
double ymax = 10.,
int verbLevel=0 ) {
gStyle->SetOptStat(0) ;
//--- make output directory.
char command[10000] ;
sprintf( command, "basename %s", wsfile ) ;
TString wsfilenopath = gSystem->GetFromPipe( command ) ;
wsfilenopath.ReplaceAll(".root","") ;
char outputdirstr[1000] ;
sprintf( outputdirstr, "outputfiles/scans-%s", wsfilenopath.Data() ) ;
TString outputdir( outputdirstr ) ;
printf("\n\n Creating output directory: %s\n\n", outputdir.Data() ) ;
sprintf(command, "mkdir -p %s", outputdir.Data() ) ;
gSystem->Exec( command ) ;
//--- Tell RooFit to shut up about anything less important than an ERROR.
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ;
if ( verbLevel > 0 ) { printf("\n\n Verbose level : %d\n\n", verbLevel) ; }
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
if ( verbLevel > 0 ) { ws->Print() ; }
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
if ( verbLevel > 0 ) {
printf("\n\n\n ===== RooDataSet ====================\n\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
}
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
RooRealVar* rrv_mu_susy_all0lep = ws->var("mu_susy_all0lep") ;
if ( rrv_mu_susy_all0lep == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_all0lep in workspace. Quitting.\n\n\n") ;
return ;
}
//-- do BG only.
rrv_mu_susy_all0lep->setVal(0.) ;
rrv_mu_susy_all0lep->setConstant( kTRUE ) ;
//-- do a prefit.
printf("\n\n\n ====== Pre fit with unmodified nll var.\n\n") ;
RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true),Hesse(false),Minos(false),Strategy(1),PrintLevel(verbLevel));
int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ;
if ( dataSusyFixedFitCovQual < 2 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; }
double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ;
if ( verbLevel > 0 ) {
dataFitResultSusyFixed->Print("v") ;
}
printf("\n\n Nll value, from fit result : %.3f\n\n", dataFitSusyFixedNll ) ;
delete dataFitResultSusyFixed ;
//-- Construct the new POI parameter.
RooAbsReal* new_poi_rar(0x0) ;
new_poi_rar = ws->var( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ;
new_poi_rar = ws->function( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ;
return ;
}
} else {
printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
}
if ( npoiPoints <=0 ) {
printf("\n\n Quitting now.\n\n" ) ;
return ;
}
double startPoiVal = new_poi_rar->getVal() ;
//--- The RooNLLVar is NOT equivalent to what minuit uses.
// RooNLLVar* nll = new RooNLLVar("nll","nll", *likelihood, *rds ) ;
// printf("\n\n Nll value, from construction : %.3f\n\n", nll->getVal() ) ;
//--- output of createNLL IS what minuit uses, so use that.
RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;
RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
/// rrv_poiValue->setVal( poiMinVal ) ;
/// rrv_poiValue->setConstant(kTRUE) ;
RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
rrv_constraintWidth -> setVal( constraintWidth ) ;
rrv_constraintWidth -> setConstant(kTRUE) ;
if ( verbLevel > 0 ) {
printf("\n\n ======= debug likelihood print\n\n") ;
likelihood->Print("v") ;
printf("\n\n ======= debug nll print\n\n") ;
nll->Print("v") ;
}
//----------------------------------------------------------------------------------------------
RooMinuit* rminuit( 0x0 ) ;
RooMinuit* rminuit_uc = new RooMinuit( *nll ) ;
rminuit_uc->setPrintLevel(verbLevel-1) ;
rminuit_uc->setNoWarn() ;
rminuit_uc->migrad() ;
rminuit_uc->hesse() ;
RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;
double floatParInitVal[10000] ;
char floatParName[10000][100] ;
int nFloatParInitVal(0) ;
RooArgList ral_floats = rfr_uc->floatParsFinal() ;
TIterator* floatParIter = ral_floats.createIterator() ;
{
RooRealVar* par ;
while ( (par = (RooRealVar*) floatParIter->Next()) ) {
sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
floatParInitVal[nFloatParInitVal] = par->getVal() ;
nFloatParInitVal++ ;
}
}
//-------
printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
double best_poi_val = new_poi_rar->getVal() ;
char minuit_formula[10000] ;
sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
nll->GetName(),
rrv_constraintWidth->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName()
) ;
printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
RooArgList( *nll,
*rrv_constraintWidth,
*new_poi_rar, *rrv_poiValue,
*new_poi_rar, *rrv_poiValue
) ) ;
printf("\n\n Current value is %.2f\n\n",
new_minuit_var->getVal() ) ;
rminuit = new RooMinuit( *new_minuit_var ) ;
RooAbsReal* plot_var = nll ;
printf("\n\n Current value is %.2f\n\n",
plot_var->getVal() ) ;
rminuit->setPrintLevel(verbLevel-1) ;
if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }
//----------------------------------------------------------------------------------------------
//-- If POI range is -1 to -1, automatically determine the range using the set value.
if ( poiMinVal < 0. && poiMaxVal < 0. ) {
printf("\n\n Automatic determination of scan range.\n\n") ;
if ( startPoiVal <= 0. ) {
printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ;
return ;
}
poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;
if ( poiMinVal < 0. ) { poiMinVal = 0. ; }
printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;
}
//----------------------------------------------------------------------------------------------
double poiVals_scanDown[1000] ;
double nllVals_scanDown[1000] ;
//-- Do scan down from best value.
printf("\n\n +++++ Starting scan down from best value.\n\n") ;
double minNllVal(1.e9) ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
nllVals_scanDown[poivi] = plot_var->getVal() ;
if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }
delete rfr ;
} // poivi
printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;
for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
RooRealVar* par = ws->var( floatParName[pi] ) ;
par->setVal( floatParInitVal[pi] ) ;
} // pi.
printf("\n\n +++++ Starting scan up from best value.\n\n") ;
//-- Now do scan up.
double poiVals_scanUp[1000] ;
double nllVals_scanUp[1000] ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
nllVals_scanUp[poivi] = plot_var->getVal() ;
if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }
delete rfr ;
} // poivi
double poiVals[1000] ;
double nllVals[1000] ;
int pointCount(0) ;
for ( int pi=0; pi<npoiPoints/2; pi++ ) {
poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
pointCount++ ;
}
for ( int pi=1; pi<npoiPoints/2; pi++ ) {
poiVals[pointCount] = poiVals_scanUp[pi] ;
nllVals[pointCount] = nllVals_scanUp[pi] ;
pointCount++ ;
}
npoiPoints = pointCount ;
printf("\n\n --- TGraph arrays:\n") ;
for ( int i=0; i<npoiPoints; i++ ) {
printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
}
printf("\n\n") ;
double nllDiffVals[1000] ;
double poiAtMinlnL(-1.) ;
double poiAtMinusDelta2(-1.) ;
double poiAtPlusDelta2(-1.) ;
for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
} // poivi
printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;
//--- Main canvas
TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
if ( cscan == 0x0 ) {
printf("\n Creating canvas.\n\n") ;
cscan = new TCanvas("cscan","Delta nll") ;
}
char gname[1000] ;
TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
sprintf( gname, "scan_%s", new_poi_name ) ;
graph->SetName( gname ) ;
double poiBest(-1.) ;
double poiMinus1stdv(-1.) ;
double poiPlus1stdv(-1.) ;
double poiMinus2stdv(-1.) ;
double poiPlus2stdv(-1.) ;
double twoDeltalnLMin(1e9) ;
int nscan(1000) ;
for ( int xi=0; xi<nscan; xi++ ) {
double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;
double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;
if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }
}
printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n",
poiBest,
(poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
(poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
) ;
printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n",
new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;
char htitle[1000] ;
sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
hscan->SetMinimum(0.) ;
hscan->SetMaximum(ymax) ;
hscan->DrawCopy() ;
graph->SetLineColor(4) ;
graph->SetLineWidth(3) ;
graph->Draw("CP") ;
gPad->SetGridx(1) ;
gPad->SetGridy(1) ;
cscan->Update() ;
TLine* line = new TLine() ;
line->SetLineColor(2) ;
line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;
TText* text = new TText() ;
text->SetTextSize(0.04) ;
char tstring[1000] ;
sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
text -> DrawTextNDC( 0.15, 0.85, tstring ) ;
sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
text -> DrawTextNDC( 0.15, 0.78, tstring ) ;
char hname[1000] ;
sprintf( hname, "hscanout_%s", new_poi_name ) ;
TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
double obsVal(-1.) ;
hsout->SetBinContent(1, obsVal ) ;
hsout->SetBinContent(2, poiPlus1stdv ) ;
hsout->SetBinContent(3, poiBest ) ;
hsout->SetBinContent(4, poiMinus1stdv ) ;
TAxis* xaxis = hsout->GetXaxis() ;
xaxis->SetBinLabel(1,"Observed val.") ;
xaxis->SetBinLabel(2,"Model+1sd") ;
xaxis->SetBinLabel(3,"Model") ;
xaxis->SetBinLabel(4,"Model-1sd") ;
char outrootfile[10000] ;
sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;
char outpdffile[10000] ;
sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;
cscan->Update() ; cscan->Draw() ;
printf("\n Saving %s\n", outpdffile ) ;
cscan->SaveAs( outpdffile ) ;
//--- save in root file
printf("\n Saving %s\n", outrootfile ) ;
TFile fout(outrootfile,"recreate") ;
graph->Write() ;
hsout->Write() ;
fout.Close() ;
delete ws ;
wstf->Close() ;
}
