import rootpy.ROOT as ROOT from rootpy.plotting import Hist1D, Hist2D from root_numpy import root2array ROOT.gROOT.ProcessLine(".x ../lhcbstyle.C") ROOT.gStyle.SetPadRightMargin(0.05) ROOT.gStyle.SetPadLeftMargin(0.21) ROOT.gStyle.SetTitleOffset(1.4, "Y") ROOT.gROOT.GetColor(3).SetRGB(0., 0.6, 0.) c = ROOT.TCanvas("c", "", 600, 500) toy_sig = root2array("test_tuple.root", branches = ["mprime", "thetaprime"], selection = "abs(md-1.97)<0.05") toy_sb = root2array("test_tuple.root", branches = ["mprime", "thetaprime"], selection = "(md<1.97-0.05)||(md>1.97+0.05)") fit_sig = root2array("fit_result_3d.root", branches = ["mprime", "thetaprime"], selection = "abs(md-1.97)<0.05") #fit_sig = root2array("fit_result.root", branches = ["mprime", "thetaprime"], selection = "md>1.97+0.05") hsig = Hist1D(100, 0., 1.) hsb = Hist1D(100, 0., 1.) hfit = Hist1D(100, 0., 1.) hsig.fill_array(toy_sig['mprime']) hsb.fill_array(toy_sb['mprime']) hfit.fill_array(fit_sig['mprime']) hsig.SetMarkerSize(0.5) hsig.Draw("e") hsig.GetXaxis().SetTitle("m'") hsig.GetYaxis().SetTitle("Entries / (0.01)") hsb.Scale(hsig.GetSumOfWeights()/hsb.GetSumOfWeights()) hsb.SetLineColor(6)
# Plot their transverse mass histMass.Fill(TVmass) # I/O if args.save: outputFile.write() outputFile.close() # -- EventLoop Ends # --Show histogram using TCanvas if (not args.save and args.TCanvas ): c1 = ROOT.TCanvas() c1.cd() histMass.GetXaxis().SetTitle("M_{T} [GeV]") histMass.GetYaxis().SetTitle("Events") histMass.Draw("Hist") dummy=input("Press Enter to continue...") # --Show histogram using Matplotlib if (not args.save and not args.TCanvas): # Set parametres for plotting plt.rcParams["figure.figsize"] = (10,6) plt.rc('xtick', labelsize=15) plt.rc('ytick', labelsize=15) plt.title("Transverse mass", fontsize=25) plt.xlabel("$M_{T} [GeV]$",fontsize=15)
import rootpy.ROOT as ROOT from rootpy.plotting import Hist1D, Hist2D from root_numpy import root2array ROOT.gROOT.ProcessLine(".x ../lhcbstyle.C") ROOT.gStyle.SetPadRightMargin(0.05) ROOT.gStyle.SetPadLeftMargin(0.21) ROOT.gStyle.SetTitleOffset(1.4, "Y") ROOT.gROOT.GetColor(3).SetRGB(0., 0.6, 0.) c = ROOT.TCanvas("c", "", 500, 450) toy_sig = root2array("test_tuple.root", branches = ["mprime", "thetaprime"]) fit_sig = root2array("eff_fit_result.root", branches = ["mprime", "thetaprime"]) hsig = Hist1D(100, 0., 1.) hfit = Hist1D(100, 0., 1.) hsig.fill_array(toy_sig['mprime']) hfit.fill_array(fit_sig['mprime']) hsig2d = Hist2D(50, 0., 1., 50, 0., 1.) hfit2d = Hist2D(50, 0., 1., 50, 0., 1.) hsig2d.fill_array(toy_sig.view((float, len(toy_sig.dtype.names)))) hfit2d.fill_array(fit_sig.view((float, len(fit_sig.dtype.names)))) hfit2d.Scale(hsig2d.GetSumOfWeights()/hfit2d.GetSumOfWeights()) sqdiff = (hfit2d-hsig2d)**2/hfit2d print(sqdiff.GetSumOfWeights()) hsig.SetMinimum(0.) hsig.SetMarkerSize(0.5)