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)
