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rf307_fullpereventerrors.py
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1## \file
2## \ingroup tutorial_roofit
3## \notebook
4## Multidimensional models: usage of full pdf with per-event errors
5##
6## \macro_code
7##
8## \date February 2018
9## \authors Clemens Lange, Wouter Verkerke (C++ version)
10
11import ROOT
12
13# B-physics pdf with per-event Gaussian resolution
14# ----------------------------------------------------------------------------------------------
15
16# Observables
17dt = ROOT.RooRealVar("dt", "dt", -10, 10)
18dterr = ROOT.RooRealVar("dterr", "per-event error on dt", 0.01, 10)
19
20# Build a gaussian resolution model scaled by the per-error =
21# gauss(dt,bias,sigma*dterr)
22bias = ROOT.RooRealVar("bias", "bias", 0, -10, 10)
23sigma = ROOT.RooRealVar(
24 "sigma", "per-event error scale factor", 1, 0.1, 10)
25gm = ROOT.RooGaussModel(
26 "gm1", "gauss model scaled bt per-event error", dt, bias, sigma, dterr)
27
28# Construct decay(dt) (x) gauss1(dt|dterr)
29tau = ROOT.RooRealVar("tau", "tau", 1.548)
30decay_gm = ROOT.RooDecay("decay_gm", "decay", dt,
31 tau, gm, ROOT.RooDecay.DoubleSided)
32
33# Construct empirical pdf for per-event error
34# -----------------------------------------------------------------
35
36# Use landau pdf to get empirical distribution with long tail
37pdfDtErr = ROOT.RooLandau("pdfDtErr", "pdfDtErr", dterr, ROOT.RooFit.RooConst(
38 1), ROOT.RooFit.RooConst(0.25))
40
41# Construct a histogram pdf to describe the shape of the dtErr distribution
43pdfErr = ROOT.RooHistPdf(
44 "pdfErr", "pdfErr", ROOT.RooArgSet(dterr), expHistDterr)
45
46# Construct conditional product decay_dm(dt|dterr)*pdf(dterr)
47# ----------------------------------------------------------------------------------------------------------------------
48
49# Construct production of conditional decay_dm(dt|dterr) with empirical
50# pdfErr(dterr)
51model = ROOT.RooProdPdf(
52 "model",
53 "model",
54 ROOT.RooArgSet(pdfErr),
55 ROOT.RooFit.Conditional(
56 ROOT.RooArgSet(decay_gm),
57 ROOT.RooArgSet(dt)))
58
59# (Alternatively you could also use the landau shape pdfDtErr)
60# ROOT.RooProdPdf model("model", "model",pdfDtErr,
61# ROOT.RooFit.Conditional(decay_gm,dt))
62
63# Sample, fit and plot product model
64# ------------------------------------------------------------------
65
66# Specify external dataset with dterr values to use model_dm as
67# conditional pdf
68data = model.generate(ROOT.RooArgSet(dt, dterr), 10000)
69
70# Fit conditional decay_dm(dt|dterr)
71# ---------------------------------------------------------------------
72
73# Specify dterr as conditional observable
74model.fitTo(data)
75
76# Plot conditional decay_dm(dt|dterr)
77# ---------------------------------------------------------------------
78
79# Make two-dimensional plot of conditional pdf in (dt,dterr)
80hh_model = model.createHistogram("hh_model", dt, ROOT.RooFit.Binning(
81 50), ROOT.RooFit.YVar(dterr, ROOT.RooFit.Binning(50)))
82hh_model.SetLineColor(ROOT.kBlue)
83
84# Make projection of data an dt
85frame = dt.frame(ROOT.RooFit.Title("Projection of model(dt|dterr) on dt"))
86data.plotOn(frame)
87model.plotOn(frame)
88
89# Draw all frames on canvas
90c = ROOT.TCanvas("rf307_fullpereventerrors",
91 "rf307_fullpereventerrors", 800, 400)
92c.Divide(2)
93c.cd(1)