ROOT   Reference Guide
rf306_condpereventerrors.py
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1## \file
2## \ingroup tutorial_roofit
3## \notebook
4##
5## Multidimensional models: complete example with use of conditional 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 fake 'external' data with per-event error
35# ------------------------------------------------------------------------------------------------------
36
37# Use landau p.d.f to get somewhat realistic distribution with long tail
38pdfDtErr = ROOT.RooLandau("pdfDtErr", "pdfDtErr", dterr, ROOT.RooFit.RooConst(
39 1), ROOT.RooFit.RooConst(0.25))
41
42# Sample data from conditional decay_gm(dt|dterr)
43# ---------------------------------------------------------------------------------------------
44
45# Specify external dataset with dterr values to use decay_dm as
46# conditional p.d.f.
47data = decay_gm.generate(ROOT.RooArgSet(
49
50# Fit conditional decay_dm(dt|dterr)
51# ---------------------------------------------------------------------
52
53# Specify dterr as conditional observable
54decay_gm.fitTo(data, ROOT.RooFit.ConditionalObservables(
55 ROOT.RooArgSet(dterr)))
56
57# Plot conditional decay_dm(dt|dterr)
58# ---------------------------------------------------------------------
59
60# Make two-dimensional plot of conditional p.d.f in (dt,dterr)
61hh_decay = decay_gm.createHistogram("hh_decay", dt, ROOT.RooFit.Binning(
62 50), ROOT.RooFit.YVar(dterr, ROOT.RooFit.Binning(50)))
63hh_decay.SetLineColor(ROOT.kBlue)
64
65# Plot decay_gm(dt|dterr) at various values of dterr
66frame = dt.frame(ROOT.RooFit.Title(
67 "Slices of decay(dt|dterr) at various dterr"))
68for ibin in range(0, 100, 20):
69 dterr.setBin(ibin)
70 decay_gm.plotOn(frame, ROOT.RooFit.Normalization(5.))
71
72# Make projection of data an dt
73frame2 = dt.frame(ROOT.RooFit.Title("Projection of decay(dt|dterr) on dt"))
74data.plotOn(frame2)
75
76# Make projection of decay(dt|dterr) on dt.
77#
78# Instead of integrating out dterr, a weighted average of curves
79# at values dterr_i as given in the external dataset.
80# (The kTRUE argument bins the data before projection to speed up the process)
82
83# Draw all frames on canvas
84c = ROOT.TCanvas("rf306_condpereventerrors",
85 "rf306_condperventerrors", 1200, 400)
86c.Divide(3)
87c.cd(1)