C++ RooDataSet::GetTitle方法代码示例

void rf403_weightedevts()
{
  // C r e a t e   o b s e r v a b l e   a n d   u n w e i g h t e d   d a t a s e t 
  // -------------------------------------------------------------------------------

  // Declare observable
  RooRealVar x("x","x",-10,10) ;
  x.setBins(40) ;

  // Construction a uniform pdf
  RooPolynomial p0("px","px",x) ;

  // Sample 1000 events from pdf
  RooDataSet* data = p0.generate(x,1000) ;

 

  // C a l c u l a t e   w e i g h t   a n d   m a k e   d a t a s e t   w e i g h t e d 
  // -----------------------------------------------------------------------------------

  // Construct formula to calculate (fake) weight for events
  RooFormulaVar wFunc("w","event weight","(x*x+10)",x) ;

  // Add column with variable w to previously generated dataset
  RooRealVar* w = (RooRealVar*) data->addColumn(wFunc) ;

  // Dataset d is now a dataset with two observable (x,w) with 1000 entries
  data->Print() ;

  // Instruct dataset wdata in interpret w as event weight rather than as observable
  RooDataSet wdata(data->GetName(),data->GetTitle(),data,*data->get(),0,w->GetName()) ;

  // Dataset d is now a dataset with one observable (x) with 1000 entries and a sum of weights of ~430K
  wdata.Print() ;



  // U n b i n n e d   M L   f i t   t o   w e i g h t e d   d a t a 
  // ---------------------------------------------------------------

  // Construction quadratic polynomial pdf for fitting
  RooRealVar a0("a0","a0",1) ;
  RooRealVar a1("a1","a1",0,-1,1) ;
  RooRealVar a2("a2","a2",1,0,10) ;
  RooPolynomial p2("p2","p2",x,RooArgList(a0,a1,a2),0) ;

  // Fit quadratic polynomial to weighted data

  // NOTE: A plain Maximum likelihood fit to weighted data does in general 
  //       NOT result in correct error estimates, unless individual
  //       event weights represent Poisson statistics themselves.
  //       
  // Fit with 'wrong' errors
  RooFitResult* r_ml_wgt = p2.fitTo(wdata,Save()) ;
  
  // A first order correction to estimated parameter errors in an 
  // (unbinned) ML fit can be obtained by calculating the
  // covariance matrix as
  //
  //    V' = V C-1 V
  //
  // where V is the covariance matrix calculated from a fit
  // to -logL = - sum [ w_i log f(x_i) ] and C is the covariance
  // matrix calculated from -logL' = -sum [ w_i^2 log f(x_i) ] 
  // (i.e. the weights are applied squared)
  //
  // A fit in this mode can be performed as follows:

  RooFitResult* r_ml_wgt_corr = p2.fitTo(wdata,Save(),SumW2Error(kTRUE)) ;



  // P l o t   w e i g h e d   d a t a   a n d   f i t   r e s u l t 
  // ---------------------------------------------------------------

  // Construct plot frame
  RooPlot* frame = x.frame(Title("Unbinned ML fit, binned chi^2 fit to weighted data")) ;

  // Plot data using sum-of-weights-squared error rather than Poisson errors
  wdata.plotOn(frame,DataError(RooAbsData::SumW2)) ;

  // Overlay result of 2nd order polynomial fit to weighted data
  p2.plotOn(frame) ;



  // M L  F i t   o f   p d f   t o   e q u i v a l e n t  u n w e i g h t e d   d a t a s e t
  // -----------------------------------------------------------------------------------------
  
  // Construct a pdf with the same shape as p0 after weighting
  RooGenericPdf genPdf("genPdf","x*x+10",x) ;

  // Sample a dataset with the same number of events as data
  RooDataSet* data2 = genPdf.generate(x,1000) ;

  // Sample a dataset with the same number of weights as data
  RooDataSet* data3 = genPdf.generate(x,43000) ;

  // Fit the 2nd order polynomial to both unweighted datasets and save the results for comparison
  RooFitResult* r_ml_unw10 = p2.fitTo(*data2,Save()) ;
//.........这里部分代码省略.........

void MakePlots(RooWorkspace* ws){

  // Here we make plots of the discriminating variable (invMass) after the fit
  // and of the control variable (isolation) after unfolding with sPlot.
  std::cout << "make plots" << std::endl;

  // make our canvas
  TCanvas* cdata = new TCanvas("sPlot","sPlot demo", 400, 600);
  cdata->Divide(1,3);

  // get what we need out of the workspace
  RooAbsPdf* model = ws->pdf("model");
  RooAbsPdf* zModel = ws->pdf("zModel");
  RooAbsPdf* qcdModel = ws->pdf("qcdModel");

  RooRealVar* isolation = ws->var("isolation");
  RooRealVar* invMass = ws->var("invMass");

  // note, we get the dataset with sWeights
  RooDataSet* data = (RooDataSet*) ws->data("dataWithSWeights");

  // this shouldn't be necessary, need to fix something with workspace
  // do this to set parameters back to their fitted values.
  model->fitTo(*data, Extended() );

  //plot invMass for data with full model and individual componenets overlayed
  //  TCanvas* cdata = new TCanvas();
  cdata->cd(1);
  RooPlot* frame = invMass->frame() ; 
  data->plotOn(frame ) ; 
  model->plotOn(frame) ;   
  model->plotOn(frame,Components(*zModel),LineStyle(kDashed), LineColor(kRed)) ;   
  model->plotOn(frame,Components(*qcdModel),LineStyle(kDashed),LineColor(kGreen)) ;   
    
  frame->SetTitle("Fit of model to discriminating variable");
  frame->Draw() ;
 

  // Now use the sWeights to show isolation distribution for Z and QCD.  
  // The SPlot class can make this easier, but here we demonstrait in more
  // detail how the sWeights are used.  The SPlot class should make this 
  // very easy and needs some more development.

  // Plot isolation for Z component.  
  // Do this by plotting all events weighted by the sWeight for the Z component.
  // The SPlot class adds a new variable that has the name of the corresponding
  // yield + "_sw".
  cdata->cd(2);

  // create weightfed data set 
  RooDataSet * dataw_z = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"zYield_sw") ;

  RooPlot* frame2 = isolation->frame() ; 
  dataw_z->plotOn(frame2, DataError(RooAbsData::SumW2) ) ; 
    
  frame2->SetTitle("isolation distribution for Z");
  frame2->Draw() ;

  // Plot isolation for QCD component.  
  // Eg. plot all events weighted by the sWeight for the QCD component.
  // The SPlot class adds a new variable that has the name of the corresponding
  // yield + "_sw".
  cdata->cd(3);
  RooDataSet * dataw_qcd = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"qcdYield_sw") ;
  RooPlot* frame3 = isolation->frame() ; 
  dataw_qcd->plotOn(frame3,DataError(RooAbsData::SumW2) ) ; 
    
  frame3->SetTitle("isolation distribution for QCD");
  frame3->Draw() ;

  //  cdata->SaveAs("SPlot.gif");

}

void ws_v03()
{
    gROOT->ProcessLine(".x ./mystyle.C");

    TFile *f1 = new TFile("K1_1270/ws_K1_1270.root");
    TFile *f2 = new TFile("K1_1400/ws_K1_1400.root");
    TFile *f3 = new TFile("K2_1430/ws_K2_1430.root");

    RooWorkspace* ws_K1_1270 = (RooWorkspace*) f1->Get("ws_K1_1270");
    RooWorkspace* ws_K1_1400 = (RooWorkspace*) f2->Get("ws_K1_1400");
    RooWorkspace* ws_K2_1430 = (RooWorkspace*) f3->Get("ws_K2_1430");
    ws_K1_1270->Print();
    ws_K1_1400->Print();
    ws_K2_1430->Print();

    // Importing variables from workspaces

    RooRealVar* m_Kpipi = ws_K1_1270 -> var("m_Kpipi");
    RooAbsPdf* totalPdf_K1_1270 = ws_K1_1270 -> pdf("totalPdf_K1_1270");
    RooAbsData* data_K1_1270 = ws_K1_1270 -> data("totalPdf_K1_1270Data");
    RooHistPdf* pdf_K1_1270_to_Krho = ws_K1_1270 -> pdf("histPdf_K1toKrho");

    /*RooRealVar* m_Kpipi = ws_K1_1400 -> var("m_Kpipi");*/
    /*RooAbsPdf* totalPdf_K1_1400 = ws_K1_1400 -> pdf("totalPdf_K1_1400");*/
    /*RooAbsData* data_K1_1400 = ws_K1_1400 -> data("totalPdf_K1_1400Data");*/
    /*RooHistPdf* pdf_K1_1400_to_Krho = ws_K1_1400 -> pdf("histPdf_K1_1400toKrho");*/

    /*RooRealVar* m_Kpipi = ws_K2_1430 -> var("m_Kpipi");*/
    /*RooAbsPdf* totalPdf_K2_1430 = ws_K2_1430 -> pdf("totalPdf_K2_1430");*/
    /*RooAbsData* data_K2_1430 = ws_K2_1430 -> data("totalPdf_K2_1430Data");*/
    /*RooHistPdf* pdf_K2_1430_to_Krho = ws_K2_1430 -> pdf("histPdf_K2_1430toKrho");*/

    // Plotting pdf

    /*TCanvas* c1 = new TCanvas("c1","canvas",20,20,1200,600);*/
    /*c1->Divide(3,1);*/
    /*c1->cd(1);*/
    /*RooPlot* frame_K1_1270 = m_Kpipi -> frame(Bins(200),Title("K1(1270) -> K#pi#pi"));*/
    /*data_K1_1270->plotOn(frame_K1_1270,DrawOption("C"));*/
    /*frame_K1_1270->Draw();*/
    /*c1->cd(2);*/
    /*RooPlot* frame_K1_1400 = m_Kpipi -> frame(Bins(200),Title("K1(1400) -> K#pi#pi"));*/
    /*data_K1_1400->plotOn(frame_K1_1400,DrawOption("C"));*/
    /*frame_K1_1400->Draw();*/
    /*c1->cd(3);*/
    /*RooPlot* frame_K2_1430= m_Kpipi -> frame(Bins(200),Title("K2*(1430) -> K#pi#pi"));*/
    /*data_K2_1430->plotOn(frame_K2_1430,DrawOption("C"));*/
    /*frame_K2_1430->Draw();*/

 ////////////////////////////////////////////////////////////////////////

    TFile *Fworkspace = new TFile("workspace.root");

    RooWorkspace* wsp = (RooWorkspace*) Fworkspace->Get("wsp");
    wsp->Print();

    RooRealVar* B_postcalib_M = wsp -> var("B_postcalib_M");
    RooRealVar* nsig_sw = wsp-> var("nsig_sw");
    RooRealVar* nbkg_sw = wsp-> var("nbkg_sw");
    RooRealVar* B_M13_Subst3_gamma2pi0 = wsp-> var("B_M13_Subst3_gamma2pi0");
    RooRealVar* B_M023 = wsp -> var("B_M023");
    RooRealVar* K_1_1270_plus_M = wsp -> var("K_1_1270_plus_M");
    RooRealVar* K_1_1270_plus_SMALLESTDELTACHI2 = wsp -> var("K_1_1270_plus_SMALLESTDELTACHI2");
    RooRealVar* gamma_CL = wsp -> var("gamma_CL");
    RooRealVar* piminus_PIDK = wsp -> var("piminus_PIDK");
    RooRealVar* piplus_PIDK = wsp -> var("piplus_PIDK");
    RooRealVar* Kplus_PIDp = wsp -> var("Kplus_PIDp");
    RooRealVar* Kplus_PIDK = wsp -> var("Kplus_PIDK");
    RooRealVar* B_M02 = wsp -> var("B_M02");
    RooRealVar* L_nsig = wsp -> var("L_nsig");
    RooRealVar* L_nbkg = wsp -> var("L_nbkg");

    RooArgSet arg(*B_postcalib_M,*gamma_CL,*B_M13_Subst3_gamma2pi0,*B_M023,*piminus_PIDK,*piplus_PIDK,*Kplus_PIDK,*Kplus_PIDp);
    arg.add(*K_1_1270_plus_M);
    arg.add(*K_1_1270_plus_SMALLESTDELTACHI2);
    arg.add(*B_M02);
    arg.add(*nsig_sw);
    arg.add(*L_nsig);
    arg.add(*nbkg_sw);
    arg.add(*L_nbkg);

    arg.add(*m_Kpipi);


    RooDataSet* DataSWeights = (RooDataSet*) wsp -> data("DataSWeights");
    RooFormulaVar newMass("m_Kpipi", "m_Kpipi", "K_1_1270_plus_M", RooArgList(*(wsp->var("K_1_1270_plus_M"))));
    DataSWeights->addColumn(newMass);
    RooDataSet* splot = new RooDataSet(DataSWeights->GetName(),DataSWeights->GetTitle(),DataSWeights,RooArgSet(arg),"","nsig_sw");


    // Defining here pdfs for other resonances to be fitted

// TRY TO ADD 2 SIMPLE BWs FOR K1 1270 AND K1 1400 AS WELL
// also, binned clone and try to fit a toy dataset

        // K1(1270)

    /*Double_t R = 0.0015; //  was 3.1 GeV-1*/
    /*RooRealVar mean_K1_1270("mean_K1_1270","",1272.,1262.,1282.);*/
    /*RooRealVar width_K1_1270("width_K1_1270","",90.,70.,110.);*/
//.........这里部分代码省略.........

//.........这里部分代码省略.........
    wsp->Print();

    RooRealVar* B_postcalib_M = wsp -> var("B_postcalib_M");
    RooRealVar* nsig_sw = wsp-> var("nsig_sw");
    RooRealVar* nbkg_sw = wsp-> var("nbkg_sw");
    RooRealVar* B_M13_Subst3_gamma2pi0 = wsp-> var("B_M13_Subst3_gamma2pi0");
    RooRealVar* B_M023 = wsp -> var("B_M023");
    RooRealVar* K_1_1270_plus_M = wsp -> var("K_1_1270_plus_M");
    RooRealVar* K_1_1270_plus_SMALLESTDELTACHI2 = wsp -> var("K_1_1270_plus_SMALLESTDELTACHI2");
    RooRealVar* gamma_CL = wsp -> var("gamma_CL");
    RooRealVar* piminus_PIDK = wsp -> var("piminus_PIDK");
    RooRealVar* piplus_PIDK = wsp -> var("piplus_PIDK");
    RooRealVar* Kplus_PIDp = wsp -> var("Kplus_PIDp");
    RooRealVar* Kplus_PIDK = wsp -> var("Kplus_PIDK");
    RooRealVar* B_M02 = wsp -> var("B_M02");
    RooRealVar* L_nsig = wsp -> var("L_nsig");
    RooRealVar* L_nbkg = wsp -> var("L_nbkg");

    RooArgSet arg(*B_postcalib_M,*gamma_CL,*B_M13_Subst3_gamma2pi0,*B_M023,*piminus_PIDK,*piplus_PIDK,*Kplus_PIDK,*Kplus_PIDp);
    arg.add(*K_1_1270_plus_M);
    arg.add(*K_1_1270_plus_SMALLESTDELTACHI2);
    arg.add(*B_M02);
    arg.add(*nsig_sw);
    arg.add(*L_nsig);
    arg.add(*nbkg_sw);
    arg.add(*L_nbkg);

    arg.add(*m_Kpipi);


    RooDataSet* DataSWeights = (RooDataSet*) wsp -> data("DataSWeights");
    RooFormulaVar newMass("m_Kpipi", "m_Kpipi", "K_1_1270_plus_M", RooArgList(*(wsp->var("K_1_1270_plus_M"))));
    DataSWeights->addColumn(newMass);
    RooDataSet* splot = new RooDataSet(DataSWeights->GetName(),DataSWeights->GetTitle(),DataSWeights,RooArgSet(arg),"","nsig_sw");


    cout << "\n\n >>>> Defining components and fitting  \n\n" << endl;

    // Defining here pdfs for other resonances to be fitted

    // K1(1270)

    Double_t R = 0.0015; //  was 3.1 GeV-1
    RooRealVar mean_K1_1270("mean_K1_1270","",1272.,1262.,1282.);
    RooRealVar width_K1_1270("width_K1_1270","",90.,70.,110.);

    // K1(1270) -> K rho
    /*RooBreitWigner totalPdf_K1_1270("totalPdf_K1_1270","",*m_Kpipi,mean_K1_1270,width_K1_1270);*/
    /*RooRelBreitWigner totalPdf_K1_1270("totalPdf_K1_1270","totalPdf_K1_1270_rho",*m_Kpipi,mean_K1_1270,width_K1_1270,RooConst(0),RooConst(R),RooConst(770.),RooConst(493.7));*/

    // K1(1270) -> K*0(1430) pi
    /*RooBreitWigner totalPdf_K1_1270_Kst0_1430("totalPdf_K1_1270_Kst0_1430","totalPdf_K1_1270_Kst0_1430",*m_Kpipi,mean_K1_1270,width_K1_1270);*/
    /*RooRelBreitWigner totalPdf_K1_1270_Kst0_1430("totalPdf_K1_1270_Kst0_1430","totalPdf_K1_1270_Kst0_1430",*m_Kpipi,mean_K1_1270,width_K1_1270,RooConst(0),RooConst(R),RooConst(1425.),RooConst(139.6));*/

    // K1(1270) -> K*0(892) pi
    /*RooBreitWigner totalPdf_K1_1270_Kst0_892("totalPdf_K1_1270_Kst0_892","totalPdf_K1_1270_Kst0_892",*m_Kpipi,mean_K1_1270,width_K1_1270);*/
    /*RooRelBreitWigner totalPdf_K1_1270_Kst0_892("totalPdf_K1_1270_Kst0_892","totalPdf_K1_1270_Kst0_892",*m_Kpipi,mean_K1_1270,width_K1_1270,RooConst(0),RooConst(R),RooConst(895.5),RooConst(139.6));*/

    // K1(1400)

    RooRealVar mean_K1_1400("mean_K1_1400","",1403./*,1396.,1410.*/);
    RooRealVar width_K1_1400("width_K1_1400","",174./*,151.,197.*/);
    RooBreitWigner totalPdf_K1_1400("totalPdf_K1_1400","",*m_Kpipi,mean_K1_1400,width_K1_1400);
    /*[>RooRelBreitWigner totalPdf_K1_1400("totalPdf_K1_1400","totalPdf_K1_1400",*m_Kpipi,mean_K1_1400,width_K1_1400,RooConst(0),RooConst(R),RooConst(895.),RooConst(139.6)); //K*(892) pi is 93%<]*/

    // K2*(1430)

//.........这里部分代码省略.........
    /*RooArgSet param(*m_Kpipi);*/
    /*RooDataSet MCdataset("MCdataset","MCdataset",param);*/

    /*Double_t K_1_1270_plus_TRUEM = 0.;*/

    /*t_tree->SetBranchAddress("K_1_1270_plus_TRUEM",&K_1_1270_plus_TRUEM);*/

    /*Int_t n = 0;*/
    /*for (int i=0;i<t_tree->GetEntries();i++)*/
    /*{*/
        /*t_tree->GetEntry(i);*/
        /**m_Kpipi_K1_1270 = K_1_1270_plus_TRUEM;*/

        /*MCdataset.add(param);*/
        /*n++;*/
    /*}*/

    /*pdf_K1_1270_to_Krho.fitTo(MCdataset,Extended(true));*/

    /*TCanvas* canvas = new TCanvas("canvas","MC data for K1(1270) -> #rho K",20,20,800,600);*/
    /*RooPlot* frame_MCdataset = m_Kpipi.frame(Bins(200));*/
    /*pdf_K1_1270_to_Krho.paramOn(frame_MCdataset);*/
    /*MCdataset.plotOn(frame_MCdataset,DrawOption("C"));*/
    /*pdf_K1_1270_to_Krho->plotOn(frame_MCdataset);*/
    /*frame_MCdataset->Draw();*/

 ////////////////////////////////////////////////////////////////////////

    TFile *Fworkspace = new TFile("workspace.root");

    RooWorkspace* wsp = (RooWorkspace*) Fworkspace->Get("wsp");
    wsp->Print();

    RooRealVar* B_postcalib_M = wsp -> var("B_postcalib_M");
    RooRealVar* nsig_sw = wsp-> var("nsig_sw");
    RooRealVar* nbkg_sw = wsp-> var("nbkg_sw");
    RooRealVar* B_M13_Subst3_gamma2pi0 = wsp-> var("B_M13_Subst3_gamma2pi0");
    RooRealVar* B_M023 = wsp -> var("B_M023");
    RooRealVar* K_1_1270_plus_M = wsp -> var("K_1_1270_plus_M");
    RooRealVar* K_1_1270_plus_SMALLESTDELTACHI2 = wsp -> var("K_1_1270_plus_SMALLESTDELTACHI2");
    RooRealVar* gamma_CL = wsp -> var("gamma_CL");
    RooRealVar* piminus_PIDK = wsp -> var("piminus_PIDK");
    RooRealVar* piplus_PIDK = wsp -> var("piplus_PIDK");
    RooRealVar* Kplus_PIDp = wsp -> var("Kplus_PIDp");
    RooRealVar* Kplus_PIDK = wsp -> var("Kplus_PIDK");
    RooRealVar* B_M02 = wsp -> var("B_M02");
    RooRealVar* L_nsig = wsp -> var("L_nsig");
    RooRealVar* L_nbkg = wsp -> var("L_nbkg");

    RooArgSet arg(*B_postcalib_M,*gamma_CL,*B_M13_Subst3_gamma2pi0,*B_M023,*piminus_PIDK,*piplus_PIDK,*Kplus_PIDK,*Kplus_PIDp);
    arg.add(*K_1_1270_plus_M);
    arg.add(*K_1_1270_plus_SMALLESTDELTACHI2);
    arg.add(*B_M02);
    arg.add(*nsig_sw);
    arg.add(*L_nsig);
    arg.add(*nbkg_sw);
    arg.add(*L_nbkg);

    arg.add(*m_Kpipi);


    RooDataSet* DataSWeights = (RooDataSet*) wsp -> data("DataSWeights");
    RooFormulaVar newMass("m_Kpipi", "m_Kpipi", "K_1_1270_plus_M", RooArgList(*(wsp->var("K_1_1270_plus_M"))));
    DataSWeights->addColumn(newMass);
    RooDataSet* splot = new RooDataSet(DataSWeights->GetName(),DataSWeights->GetTitle(),DataSWeights,RooArgSet(arg),"","nsig_sw");

    // here add pdfs and FIT sum of pdf to splot

    RooRealVar K1_1270_y("K1_1270_y","K1_1270_y",1000.,0.,10000.);
    RooRealVar K1_1400_y("K1_1400_y","K1_1400_y",100.,0.,10000.);
    /*RooRealVar K2_1430_y("K2_1430_y","K2_1430_y",300.,0.,10000.);*/
    RooFormulaVar K2_1430_y("K2_1430_y","K2_1430_y","K1_1270_y/3.",K1_1270_y); // K2_1430 yield is fixed to 1/3 of the K1_1270 yield (as found from Belle ... TO CHECK)

    RooArgList shapes;
    RooArgList yields;
    shapes.add(*totalPdf_K1_1270);
    shapes.add(*totalPdf_K1_1400);
    shapes.add(*totalPdf_K2_1430);
    yields.add(K1_1270_y);
    yields.add(K1_1400_y);
    yields.add(K2_1430_y);

    RooAddPdf PDF("PDF","total Pdf for the resonances considered", shapes,yields);

    PDF->fitTo(*splot,Extended(),SumW2Error(kTRUE),Range(1000,2000));

    // Plotting

    TCanvas* canvas_sPlot = new TCanvas("canvas_sPlot","sPlot with weights",40,20,800,600);
    RooPlot* frame_splot = m_Kpipi->frame(1000,2000,80);
    splot->plotOn(frame_splot);

    PDF->paramOn(frame_splot);
    PDF->plotOn(frame_splot,Components(*totalPdf_K1_1270),LineColor(kRed),LineStyle(kDashed));
    PDF->plotOn(frame_splot,Components(*totalPdf_K1_1400),LineColor(1),LineStyle(kDashed));
    PDF->plotOn(frame_splot,Components(*totalPdf_K2_1430),LineColor(51),LineStyle(kDashed));
    PDF->plotOn(frame_splot);
    frame_splot->Draw();

}