Rf 1 0 8_Plotbinning

Basic functionality: plotting unbinned data with alternate and variable binnings

Author: Clemens Lange, Wouter Verkerke (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, January 17, 2022 at 09:52 AM.

In [ ]:
import ROOT

Set up model

Build a B decay pdf with mixing

In [ ]:
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", 0.0)

mixState = ROOT.RooCategory("mixState", "B0/B0bar mixing state", {"mixed": -1, "unmixed": 1})
tagFlav = ROOT.RooCategory("tagFlav", "Flavour of the tagged B0", {"B0": 1, "B0bar": -1})

Build a gaussian resolution model

In [ ]:
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

In [ ]:
bmix = ROOT.RooBMixDecay("bmix", "decay", dt, mixState, tagFlav, tau, dm, w, dw, gm1, type="DoubleSided")

Sample data from model

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

In [ ]:
data = bmix.generate({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)

In [ ]:
tbins = ROOT.RooBinning(-15, 15)

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

In [ ]:
tbins.addUniform(60, -15, 0)

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

In [ ]:
tbins.addUniform(15, 0, 15)

Make plot with specified binning

In [ ]:
dtframe = dt.frame(Range=(-15, 15), Title="dt distribution with custom binning")
data.plotOn(dtframe, 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)

In [ ]:
abins = ROOT.RooBinning(-10, 10)

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

In [ ]:
abins.addBoundary(0)
abins.addBoundaryPair(1)
abins.addBoundaryPair(2)
abins.addBoundaryPair(3)
abins.addBoundaryPair(4)
abins.addBoundaryPair(6)

Create plot frame in dt

In [ ]:
aframe = dt.frame(Range=(-10, 10), Title="mixState asymmetry distribution with custom binning")

Plot mixState asymmetry of data with specified customg binning

In [ ]:
data.plotOn(aframe, Asymmetry=mixState, Binning=abins)

Plot corresponding property of pdf

In [ ]:
bmix.plotOn(aframe, Asymmetry=mixState)

Adjust vertical range of plot to sensible values for an asymmetry

In [ ]:
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

In [ ]:
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")

Draw all canvases

In [ ]:
from ROOT import gROOT 
gROOT.GetListOfCanvases().Draw()