doc/master/rf307__fullpereventerrors_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 empirical pdf for per-event error

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


# Use landau pdf to get empirical distribution with long tail

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

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


# Construct a histogram pdf to describe the shape of the dtErr distribution

expHistDterr = expDataDterr.binnedClone()

pdfErr = ROOT.RooHistPdf("pdfErr", "pdfErr", {dterr}, expHistDterr)


# Construct conditional product decay_dm(dt|dterr)*pdf(dterr)

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


# Construct production of conditional decay_dm(dt|dterr) with empirical

# pdfErr(dterr)

model = ROOT.RooProdPdf("model", "model", {pdfErr}, Conditional=({decay_gm}, {dt}))


# (Alternatively you could also use the landau shape pdfDtErr)

# ROOT.RooProdPdf model("model", "model",pdfDtErr,

# ROOT.RooFit.Conditional(decay_gm,dt))


# Sample, fit and plot product model

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


# Specify external dataset with dterr values to use model_dm as

# conditional pdf

data = model.generate({dt, dterr}, 10000)


# Fit conditional decay_dm(dt|dterr)

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


# Specify dterr as conditional observable

model.fitTo(data, PrintLevel=-1)


# Plot conditional decay_dm(dt|dterr)

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


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

hh_model = model.createHistogram("hh_model", dt, Binning=50, YVar=dict(var=dterr, Binning=50))

hh_model.SetLineColor(ROOT.kBlue)


# Make projection of data an dt

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

data.plotOn(frame)

model.plotOn(frame)


# Draw all frames on canvas

c = ROOT.TCanvas("rf307_fullpereventerrors", "rf307_fullpereventerrors", 800, 400)

c.Divide(2)

c.cd(1)

ROOT.gPad.SetLeftMargin(0.20)

hh_model.GetZaxis().SetTitleOffset(2.5)

hh_model.Draw("surf")

c.cd(2)

ROOT.gPad.SetLeftMargin(0.15)

frame.GetYaxis().SetTitleOffset(1.6)

frame.Draw()


c.SaveAs("rf307_fullpereventerrors.png")