C++ TGraph2D::SetMarkerStyle方法代码示例

void setGraphOptions(TGraph2D &g)
{
  g.SetTitle("");
  g.SetMarkerColor(1);
  g.SetMarkerStyle(24);
  g.SetMarkerSize(.5);
}

//.........这里部分代码省略.........
			// lineZY->SetLineColor(kGreen);
			// lineZY->Draw("same");


			// 3D FITTING CODE (based on line3Dfit.C), draw graph and line fit
			ROOT::Fit::Fitter  fitter;
		   	SumDistance2 sdist(graph);
#ifdef __CINT__
		   	ROOT::Math::Functor fcn(&sdist,4,"SumDistance2");
#else
		   	ROOT::Math::Functor fcn(sdist,4);
#endif
			// set the function and the initial parameter values
			double pStart[4] = {param0,param1,param2,param3};
			fitter.SetFCN(fcn,pStart);
			// set step sizes different than default ones (0.3 times parameter values)
			for (int i = 0; i < 4; ++i) fitter.Config().ParSettings(i).SetStepSize(0.01);

			bool ok = fitter.FitFCN();
			if (!ok) {
			  Error("line3Dfit","Line3D Fit failed");
			  fitFailed = true;
			} else {
				const ROOT::Fit::FitResult & result = fitter.Result();
				const double * fitParams = result.GetParams();

				sumSquares = result.MinFcnValue();
				std::cout << "Sum of distance squares:  " << sumSquares << std::endl;
				std::cout << "Sum of distance squares divided by numEntries: " << sumSquares/numEntries << std::endl;
				std::cout << "Theta : " << TMath::ATan(sqrt(pow(fitParams[1], 2) + pow(fitParams[3], 2))) << std::endl;
				// result.Print(std::cout); // (un)suppress results output

				// Draw the graph
				graph->SetMarkerStyle(8);
				graph->SetMarkerSize(0.5);
				graph->Draw("pcol");

				// Draw the fitted line
				int n = 1000;
				double t0 = 0; // t is the z coordinate
				double dt = 40.96;
				TPolyLine3D *l = new TPolyLine3D(n);
				for (int i = 0; i <n;++i) {
				  double t = t0+ dt*i/n;
				  double x,y,z;
				  line(t,fitParams,x,y,z);
				  l->SetPoint(i,x,y,z);
				}
				l->SetLineColor(kRed);
				l->Draw("same");

				// Access fit params and minfcnvalue
				// cout << "FIT1: " << fitParams[1] << "\n";
				// cout << "FIT2: " << result.MinFcnValue() << "\n";
			}

			// Criteria to be a good event (if not good entry, then don't show)
			bool isGoodEvent = false;

				// the following block of code finds the mean X, Y ans Z values
				double meanX = 0;
				double meanY = 0;
				double meanZ = 0;
				reader->SetEntry(startEntryNum);
				for (int i = 0; i < endEntryNum - startEntryNum; i++) {
					meanX += graph->GetX()[i];

void kdTreeBinning() {

   // -----------------------------------------------------------------------------------------------
   //  C r e a t e  r a n d o m  s a m p l e  w i t h  r e g u l a r  b i n n i n g  p l o t t i n g
   // -----------------------------------------------------------------------------------------------

   const UInt_t DATASZ = 10000;
   const UInt_t DATADIM = 2;
   const UInt_t NBINS = 50;

   Double_t smp[DATASZ * DATADIM];

   double mu[2] = {0,2};
   double sig[2] = {2,3};
   TRandom3 r;
   r.SetSeed(1);
   for (UInt_t i = 0; i < DATADIM; ++i)
      for (UInt_t j = 0; j < DATASZ; ++j)
         smp[DATASZ * i + j] = r.Gaus(mu[i], sig[i]);

   UInt_t h1bins = (UInt_t) sqrt(NBINS);

   TH2D* h1 = new TH2D("h1BinTest", "Regular binning", h1bins, -5., 5., h1bins, -5., 5.);
   for (UInt_t j = 0; j < DATASZ; ++j)
      h1->Fill(smp[j], smp[DATASZ + j]);


   // ---------------------------------------------------------------------------------------------
   // C r e a t e  K D T r e e B i n n i n g  o b j e c t  w i t h  T H 2 P o l y  p l o t t i n g
   // ---------------------------------------------------------------------------------------------

   TKDTreeBinning* kdBins = new TKDTreeBinning(DATASZ, DATADIM, smp, NBINS);

   UInt_t nbins = kdBins->GetNBins();
   UInt_t dim   = kdBins->GetDim();

   const Double_t* binsMinEdges = kdBins->GetBinsMinEdges();
   const Double_t* binsMaxEdges = kdBins->GetBinsMaxEdges();

   TH2Poly* h2pol = new TH2Poly("h2PolyBinTest", "KDTree binning", kdBins->GetDataMin(0), kdBins->GetDataMax(0), kdBins->GetDataMin(1), kdBins->GetDataMax(1));

   for (UInt_t i = 0; i < nbins; ++i) {
      UInt_t edgeDim = i * dim;
      h2pol->AddBin(binsMinEdges[edgeDim], binsMinEdges[edgeDim + 1], binsMaxEdges[edgeDim], binsMaxEdges[edgeDim + 1]);
   }

   for (UInt_t i = 1; i <= kdBins->GetNBins(); ++i)
      h2pol->SetBinContent(i, kdBins->GetBinDensity(i - 1));

   std::cout << "Bin with minimum density: " << kdBins->GetBinMinDensity() << std::endl;
   std::cout << "Bin with maximum density: " << kdBins->GetBinMaxDensity() << std::endl;

   TCanvas* c1 = new TCanvas("glc1", "TH2Poly from a kdTree",0,0,600,800);
   c1->Divide(1,3);
   c1->cd(1);
   h1->Draw("lego");

   c1->cd(2);
   h2pol->Draw("COLZ L");
   c1->Update();


   /* Draw an equivalent plot showing the data points */
   /*-------------------------------------------------*/

   std::vector<Double_t> z = std::vector<Double_t>(DATASZ, 0.);
   for (UInt_t i = 0; i < DATASZ; ++i)
      z[i] = (Double_t) h2pol->GetBinContent(h2pol->FindBin(smp[i], smp[DATASZ + i]));

   TGraph2D *g = new TGraph2D(DATASZ, smp, &smp[DATASZ], &z[0]);
   gStyle->SetPalette(1);
   g->SetMarkerStyle(20);

   c1->cd(3);
   g->Draw("pcol");
   c1->Update();

   // ---------------------------------------------------------
   // make a new TH2Poly where bins are ordered by the density
   // ---------------------------------------------------------

   TH2Poly* h2polrebin = new TH2Poly("h2PolyBinTest", "KDTree binning", kdBins->GetDataMin(0), kdBins->GetDataMax(0), kdBins->GetDataMin(1), kdBins->GetDataMax(1));
   h2polrebin->SetFloat();

   /*---------------------------------*/
   /* Sort the bins by their density  */
   /*---------------------------------*/

   kdBins->SortBinsByDensity();

   for (UInt_t i = 0; i < kdBins->GetNBins(); ++i) {
      const Double_t* binMinEdges = kdBins->GetBinMinEdges(i);
      const Double_t* binMaxEdges = kdBins->GetBinMaxEdges(i);
      h2polrebin->AddBin(binMinEdges[0], binMinEdges[1], binMaxEdges[0], binMaxEdges[1]);
   }

   for (UInt_t i = 1; i <= kdBins->GetNBins(); ++i){
      h2polrebin->SetBinContent(i, kdBins->GetBinDensity(i - 1));}

   std::cout << "Bin with minimum density: " << kdBins->GetBinMinDensity() << std::endl;
//.........这里部分代码省略.........

//.........这里部分代码省略.........
    c1->cd(11);
    //c1_5->SetTicks(0,0);
    //c1_2->SetRightMargin(0.15);
    //c1_2->SetLeftMargin(0.15);
    //c1_2->SetBottomMargin(0.02);
    lm5->Draw("colz1");
    text->DrawLatex(-3.,0.7,"#kappa_{#lambda} = -5, c_{g}  = c_{2} = 0");  
    c1->cd(12);
    //c1_5->SetTicks(0,0);
    //c1_2->SetRightMargin(0.15);
    //c1_2->SetLeftMargin(0.15);
    //c1_2->SetBottomMargin(0.02);
    lm10->Draw("colz1");
    //text->DrawLatex(-3,1,"SM plane in log scale");
    //text->SetTextSize(0.08);
    //text->SetTextColor(0);
    text->DrawLatex(-3.,0.7,"#kappa_{#lambda} = -10, c_{g}  = c_{2} = 0");  
    c1->SaveAs("C2Fit.pdf");
    c1->Close();
     */
    //////////////////////////////////////////////////
    //
    // do histrograms with errors
    //
    // plot (point - fit)/fit between int nmintest, int nmaxtest
    // do by the planes
    //////////////////////////////////////////////////
    // take the fit
    // need to be done by planes
    //c1->Clear();
    // a
    double SMxs =  0.013531; // 1 0.017278;// 14 0.0041758;// 8tev 0.013531; // 13 tev 0.017278;// 0.0041758;
    TGraph2D *g2 = new TGraph2D(117);//(118);
    g2->SetMarkerStyle(20);
    g2->SetMarkerSize(2);
    g2->SetTitle("0");
    g2->SetTitle("#kappa_{t} = #kappa_{#lambda} = 1 , c_{2} = 0 ; c_{g} ; c_{2g}");
    int j=0;
    for (unsigned int ij = 0; ij < nmaxx ; ij++) if( par1[ij] ==1 && par0[ij] ==1 && par2[ij]==0 && cross_section[ij] >0.0001) if(ij!=301) {
        double fit = SMxs*(fg2->Eval(par3[ij], par4[ij]));
        cout<<j<<" "<< par3[ij]<<" "<< par4[ij]<<" "<<fit <<" "<< cross_section[ij]<<" diff: " <<(fit - cross_section[ij])/fit<< endl;
        g2->SetPoint(j, par3[ij], par4[ij], 100*(fit - cross_section[ij])/fit); 
        j++;
        //Differences2->Fill(par3[i], par4[i], (fit - cross_section[i])/fit); 
    }
    // b 
    ////////////////////////////////
    int ktb=1.0;
    int klb=1.0;
    // cg ===> x ==> c2
    // c2g ===> y ==> kt ==> cg = c2g
    TF2 *pb = new TF2("pb","([0]*[15]**4 + [1]*x**2 + [2]*[15]**2*[16]**2 + [3]*y**2*[16]**2 +  [4]*y**2 + [5]*x*[15]**2 + [6]*[15]*[16]*[15]**2 + [7]*[15]*[16]*x + [8]*y*[16]*x - [9]*x*y + [10]*y*[16]*[15]**2 - [11]*y*[15]**2 + [12]*[16]*y*[15]*[16] - [13]*y*[15]*[16] - [14]*y*y*[16])/[17]",-3,3,-1,1);
    pb->SetParameter(0,a[0]);
    pb->SetParameter(1,a[1]);
    pb->SetParameter(2,a[2]);    
    pb->SetParameter(3,a[3]);
    pb->SetParameter(4,a[4]);
    pb->SetParameter(5,a[5]); 
    pb->SetParameter(6,a[6]);
    pb->SetParameter(7,a[7]);
    pb->SetParameter(8,a[8]); 
    pb->SetParameter(9,a[9]);
    pb->SetParameter(10,a[10]);
    pb->SetParameter(11,a[11]); 
    pb->SetParameter(12,a[12]);    
    pb->SetParameter(13,a[13]);

//.........这里部分代码省略.........
    fcint = fc.GetInterval();  // that was easy.

    RooFitResult* fit = modelWithConstraints->fitTo(*data, Save(true));

    // Third, use a Calculator based on Markov Chain monte carlo
    // Before configuring the calculator, let's make a ProposalFunction
    // that will achieve a high acceptance rate
    ProposalHelper ph;
    ph.SetVariables((RooArgSet&)fit->floatParsFinal());
    ph.SetCovMatrix(fit->covarianceMatrix());
    ph.SetUpdateProposalParameters(true);
    ph.SetCacheSize(100);
    ProposalFunction* pdfProp = ph.GetProposalFunction();  // that was easy

    MCMCCalculator mc(*data, modelConfig);
    mc.SetNumIters(20000); // steps to propose in the chain
    mc.SetTestSize(.05); // 95% CL
    mc.SetNumBurnInSteps(40); // ignore first N steps in chain as "burn in"
    mc.SetProposalFunction(*pdfProp);
    mc.SetLeftSideTailFraction(0.5);  // find a "central" interval
    MCMCInterval* mcInt = (MCMCInterval*)mc.GetInterval();  // that was easy


    // Get Lower and Upper limits from Profile Calculator
    cout << "Profile lower limit on s = " << ((LikelihoodInterval*) lrinterval)->LowerLimit(*s) << endl;
    cout << "Profile upper limit on s = " << ((LikelihoodInterval*) lrinterval)->UpperLimit(*s) << endl;

    // Get Lower and Upper limits from FeldmanCousins with profile construction
    if (fcint != NULL) {
        double fcul = ((PointSetInterval*) fcint)->UpperLimit(*s);
        double fcll = ((PointSetInterval*) fcint)->LowerLimit(*s);
        cout << "FC lower limit on s = " << fcll << endl;
        cout << "FC upper limit on s = " << fcul << endl;
        TLine* fcllLine = new TLine(fcll, 0, fcll, 1);
        TLine* fculLine = new TLine(fcul, 0, fcul, 1);
        fcllLine->SetLineColor(kRed);
        fculLine->SetLineColor(kRed);
        fcllLine->Draw("same");
        fculLine->Draw("same");
        dataCanvas->Update();
    }

    // Plot MCMC interval and print some statistics
    MCMCIntervalPlot mcPlot(*mcInt);
    mcPlot.SetLineColor(kMagenta);
    mcPlot.SetLineWidth(2);
    mcPlot.Draw("same");

    double mcul = mcInt->UpperLimit(*s);
    double mcll = mcInt->LowerLimit(*s);
    cout << "MCMC lower limit on s = " << mcll << endl;
    cout << "MCMC upper limit on s = " << mcul << endl;
    cout << "MCMC Actual confidence level: "
         << mcInt->GetActualConfidenceLevel() << endl;

    // 3-d plot of the parameter points
    dataCanvas->cd(2);
    // also plot the points in the markov chain
    RooDataSet * chainData = mcInt->GetChainAsDataSet();

    assert(chainData);
    std::cout << "plotting the chain data - nentries = " << chainData->numEntries() << std::endl;
    TTree* chain =  RooStats::GetAsTTree("chainTreeData","chainTreeData",*chainData);
    assert(chain);
    chain->SetMarkerStyle(6);
    chain->SetMarkerColor(kRed);

    chain->Draw("s:ratioSigEff:ratioBkgEff","nll_MarkovChain_local_","box"); // 3-d box proportional to posterior

    // the points used in the profile construction
    RooDataSet * parScanData = (RooDataSet*) fc.GetPointsToScan();
    assert(parScanData);
    std::cout << "plotting the scanned points used in the frequentist construction - npoints = " << parScanData->numEntries() << std::endl;
    // getting the tree and drawing it -crashes (very strange....);
    // TTree* parameterScan =  RooStats::GetAsTTree("parScanTreeData","parScanTreeData",*parScanData);
    // assert(parameterScan);
    // parameterScan->Draw("s:ratioSigEff:ratioBkgEff","","goff");
    TGraph2D *gr = new TGraph2D(parScanData->numEntries());
    for (int ievt = 0; ievt < parScanData->numEntries(); ++ievt) {
        const RooArgSet * evt = parScanData->get(ievt);
        double x = evt->getRealValue("ratioBkgEff");
        double y = evt->getRealValue("ratioSigEff");
        double z = evt->getRealValue("s");
        gr->SetPoint(ievt, x,y,z);
        // std::cout << ievt << "  " << x << "  " << y << "  " << z << std::endl;
    }
    gr->SetMarkerStyle(24);
    gr->Draw("P SAME");


    delete wspace;
    delete lrinterval;
    delete mcInt;
    delete fcint;
    delete data;

    // print timing info
    t.Stop();
    t.Print();
}