doc/v636/rf306__condpereventerrors_8py.html

import ROOT


# B-physics pdf with per-event Gaussian resolution

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


# Observables

dt = ROOT.RooRealVar("dt", "dt", -10, 10)

dterr = ROOT.RooRealVar("dterr", "per-event error on dt", 0.01, 10)


# Build a gaussian resolution model scaled by the per-error =

# gauss(dt,bias,sigma*dterr)

bias = ROOT.RooRealVar("bias", "bias", 0, -10, 10)

sigma = ROOT.RooRealVar("sigma", "per-event error scale factor", 1, 0.1, 10)

gm = ROOT.RooGaussModel("gm1", "gauss model scaled bt per-event error", dt, bias, sigma, dterr)


# Construct decay(dt) (x) gauss1(dt|dterr)

tau = ROOT.RooRealVar("tau", "tau", 1.548)

decay_gm = ROOT.RooDecay("decay_gm", "decay", dt, tau, gm, type="DoubleSided")


# Construct fake 'external' data with per-event error

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


# Use landau pdf to get somewhat realistic distribution with long tail

pdfDtErr = ROOT.RooLandau("pdfDtErr", "pdfDtErr", dterr, 1.0, 0.25)

expDataDterr = pdfDtErr.generate({dterr}, 10000)


# Sample data from conditional decay_gm(dt|dterr)

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


# Specify external dataset with dterr values to use decay_dm as

# conditional pdf

data = decay_gm.generate({dt}, ProtoData=expDataDterr)


# Fit conditional decay_dm(dt|dterr)

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


# Specify dterr as conditional observable

decay_gm.fitTo(data, ConditionalObservables={dterr}, PrintLevel=-1)


# Plot conditional decay_dm(dt|dterr)

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


# Make two-dimensional plot of conditional pdf in (dt,dterr)

hh_decay = decay_gm.createHistogram("hh_decay", dt, Binning=50, YVar=dict(var=dterr, Binning=50))

hh_decay.SetLineColor("kBlue")


# Plot decay_gm(dt|dterr) at various values of dterr

frame = dt.frame(Title="Slices of decay(dt|dterr) at various dterr")

for ibin in range(0, 100, 20):

    dterr.setBin(ibin)

    decay_gm.plotOn(frame, Normalization=5.0)


# Make projection of data an dt

frame2 = dt.frame(Title="Projection of decay(dt|dterr) on dt")

data.plotOn(frame2)


# Make projection of decay(dt|dterr) on dt.

#

# Instead of integrating out dterr, a weighted average of curves

# at values dterr_i as given in the external dataset.

# (The kTRUE argument bins the data before projection to speed up the process)

decay_gm.plotOn(frame2, ProjWData=(expDataDterr, True))


# Draw all frames on canvas

c = ROOT.TCanvas("rf306_condpereventerrors", "rf306_condperventerrors", 1200, 400)

c.Divide(3)

c.cd(1)

ROOT.gPad.SetLeftMargin(0.20)

hh_decay.GetZaxis().SetTitleOffset(2.5)

hh_decay.Draw("surf")

c.cd(2)

ROOT.gPad.SetLeftMargin(0.15)

frame.GetYaxis().SetTitleOffset(1.6)

frame.Draw()

c.cd(3)

ROOT.gPad.SetLeftMargin(0.15)

frame2.GetYaxis().SetTitleOffset(1.6)

frame2.Draw()


c.SaveAs("rf306_condpereventerrors.png")

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358

ROOT::Detail::TRangeCast
Definition TCollection.h:311