doc/v624/rf108__plotbinning_8py.html

import ROOT


# Set up model

# ---------------------


# Build a B decay pdf with mixing

dt = ROOT.RooRealVar("dt", "dt", -20, 20)

dm = ROOT.RooRealVar("dm", "dm", 0.472)

tau = ROOT.RooRealVar("tau", "tau", 1.547)

w = ROOT.RooRealVar("w", "mistag rate", 0.1)

dw = ROOT.RooRealVar("dw", "delta mistag rate", 0.)


mixState = ROOT.RooCategory("mixState", "B0/B0bar mixing state")

mixState.defineType("mixed", -1)

mixState.defineType("unmixed", 1)

tagFlav = ROOT.RooCategory("tagFlav", "Flavour of the tagged B0")

tagFlav.defineType("B0", 1)

tagFlav.defineType("B0bar", -1)


# Build a gaussian resolution model

dterr = ROOT.RooRealVar("dterr", "dterr", 0.1, 1.0)

bias1 = ROOT.RooRealVar("bias1", "bias1", 0)

sigma1 = ROOT.RooRealVar("sigma1", "sigma1", 0.1)

gm1 = ROOT.RooGaussModel("gm1", "gauss model 1", dt, bias1, sigma1)


# Construct Bdecay (x) gauss

bmix = ROOT.RooBMixDecay("bmix", "decay", dt, mixState, tagFlav,

                         tau, dm, w, dw, gm1, ROOT.RooBMixDecay.DoubleSided)


# Sample data from model

# --------------------------------------------


# Sample 2000 events in (dt,mixState,tagFlav) from bmix

data = bmix.generate(ROOT.RooArgSet(dt, mixState, tagFlav), 2000)


# Show dt distribution with custom binning

# -------------------------------------------------------------------------------


# Make plot of dt distribution of data in range (-15,15) with fine binning

# for dt>0 and coarse binning for dt<0


# Create binning object with range (-15,15)

tbins = ROOT.RooBinning(-15, 15)


# Add 60 bins with uniform spacing in range (-15,0)

tbins.addUniform(60, -15, 0)


# Add 15 bins with uniform spacing in range (0,15)

tbins.addUniform(15, 0, 15)


# Make plot with specified binning

dtframe = dt.frame(ROOT.RooFit.Range(-15, 15),

                   ROOT.RooFit.Title("dt distribution with custom binning"))

data.plotOn(dtframe, ROOT.RooFit.Binning(tbins))

bmix.plotOn(dtframe)


# NB: Note that bin density for each bin is adjusted to that of default frame binning as shown

# in Y axis label (100 bins -. Events/0.4*Xaxis-dim) so that all bins

# represent a consistent density distribution


# Show mixstate asymmetry with custom binning

# ------------------------------------------------------------------------------------


# Make plot of dt distribution of data asymmetry in 'mixState' with

# variable binning


# Create binning object with range (-10,10)

abins = ROOT.RooBinning(-10, 10)


# Add boundaries at 0, (-1,1), (-2,2), (-3,3), (-4,4) and (-6,6)

abins.addBoundary(0)

abins.addBoundaryPair(1)

abins.addBoundaryPair(2)

abins.addBoundaryPair(3)

abins.addBoundaryPair(4)

abins.addBoundaryPair(6)


# Create plot frame in dt

aframe = dt.frame(ROOT.RooFit.Range(-10, 10), ROOT.RooFit.Title(

    "mixState asymmetry distribution with custom binning"))


# Plot mixState asymmetry of data with specified customg binning

data.plotOn(aframe, ROOT.RooFit.Asymmetry(

    mixState), ROOT.RooFit.Binning(abins))


# Plot corresponding property of pdf

bmix.plotOn(aframe, ROOT.RooFit.Asymmetry(mixState))


# Adjust vertical range of plot to sensible values for an asymmetry

aframe.SetMinimum(-1.1)

aframe.SetMaximum(1.1)


# NB: For asymmetry distributions no density corrects are needed (and are

# thus not applied)


# Draw plots on canvas

c = ROOT.TCanvas("rf108_plotbinning", "rf108_plotbinning", 800, 400)

c.Divide(2)

c.cd(1)

ROOT.gPad.SetLeftMargin(0.15)

dtframe.GetYaxis().SetTitleOffset(1.6)

dtframe.Draw()

c.cd(2)

ROOT.gPad.SetLeftMargin(0.15)

aframe.GetYaxis().SetTitleOffset(1.6)

aframe.Draw()


c.SaveAs("rf108_plotbinning.png")