ROOT   Reference Guide
rf307_fullpereventerrors.py
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1## \file
2## \ingroup tutorial_roofit
3## \notebook
4##
5## Multidimensional models: usage of full p.d.f. with per-event errors
6##
7## \macro_code
8##
9## \date February 2018
10## \authors Clemens Lange, Wouter Verkerke (C++ version)
11
12import ROOT
13
14# B-physics pdf with per-event Gaussian resolution
15# ----------------------------------------------------------------------------------------------
16
17# Observables
18dt = ROOT.RooRealVar("dt", "dt", -10, 10)
19dterr = ROOT.RooRealVar("dterr", "per-event error on dt", 0.01, 10)
20
21# Build a gaussian resolution model scaled by the per-error =
22# gauss(dt,bias,sigma*dterr)
23bias = ROOT.RooRealVar("bias", "bias", 0, -10, 10)
24sigma = ROOT.RooRealVar(
25 "sigma", "per-event error scale factor", 1, 0.1, 10)
26gm = ROOT.RooGaussModel(
27 "gm1", "gauss model scaled bt per-event error", dt, bias, sigma, dterr)
28
29# Construct decay(dt) (x) gauss1(dt|dterr)
30tau = ROOT.RooRealVar("tau", "tau", 1.548)
31decay_gm = ROOT.RooDecay("decay_gm", "decay", dt,
32 tau, gm, ROOT.RooDecay.DoubleSided)
33
34# Construct empirical pdf for per-event error
35# -----------------------------------------------------------------
36
37# Use landau p.d.f to get empirical distribution with long tail
38pdfDtErr = ROOT.RooLandau("pdfDtErr", "pdfDtErr", dterr, ROOT.RooFit.RooConst(
39 1), ROOT.RooFit.RooConst(0.25))
41
42# Construct a histogram pdf to describe the shape of the dtErr distribution
44pdfErr = ROOT.RooHistPdf(
45 "pdfErr", "pdfErr", ROOT.RooArgSet(dterr), expHistDterr)
46
47# Construct conditional product decay_dm(dt|dterr)*pdf(dterr)
48# ----------------------------------------------------------------------------------------------------------------------
49
50# Construct production of conditional decay_dm(dt|dterr) with empirical
51# pdfErr(dterr)
52model = ROOT.RooProdPdf(
53 "model",
54 "model",
55 ROOT.RooArgSet(pdfErr),
56 ROOT.RooFit.Conditional(
57 ROOT.RooArgSet(decay_gm),
58 ROOT.RooArgSet(dt)))
59
60# (Alternatively you could also use the landau shape pdfDtErr)
61# ROOT.RooProdPdf model("model", "model",pdfDtErr,
62# ROOT.RooFit.Conditional(decay_gm,dt))
63
64# Sample, fit and plot product model
65# ------------------------------------------------------------------
66
67# Specify external dataset with dterr values to use model_dm as
68# conditional p.d.f.
69data = model.generate(ROOT.RooArgSet(dt, dterr), 10000)
70
71# Fit conditional decay_dm(dt|dterr)
72# ---------------------------------------------------------------------
73
74# Specify dterr as conditional observable
75model.fitTo(data)
76
77# Plot conditional decay_dm(dt|dterr)
78# ---------------------------------------------------------------------
79
80# Make two-dimensional plot of conditional p.d.f in (dt,dterr)
81hh_model = model.createHistogram("hh_model", dt, ROOT.RooFit.Binning(
82 50), ROOT.RooFit.YVar(dterr, ROOT.RooFit.Binning(50)))
83hh_model.SetLineColor(ROOT.kBlue)
84
85# Make projection of data an dt
86frame = dt.frame(ROOT.RooFit.Title("Projection of model(dt|dterr) on dt"))
87data.plotOn(frame)
88model.plotOn(frame)
89
90# Draw all frames on canvas
91c = ROOT.TCanvas("rf307_fullpereventerrors",
92 "rf307_fullpereventerrors", 800, 400)
93c.Divide(2)
94c.cd(1)