Variable bin widths, per-width normalisation and overflow bins¶

Bin edges may be any increasing sequence. normalize="width" divides by the bin width so the y axis reads Events / GeV, and flow="show" appends the underflow and overflow as extra bins instead of the default arrow hints.

DefaultATLASCMSLHCbALICEDUNE

Variable bin widths, per-width normalisation and overflow bins, Default style

import rootfig as rf

style = rf.Style()

edges = [0, 20, 40, 60, 80, 100, 130, 160, 200, 250, 320, 400]
rf.plot(
    mc,
    rf.Variable("MET", bins=edges, label=r"$E_T^{miss}$", unit="GeV"),
    observed=data,
    stack=True,
    ratio=True,
    normalize="width",
    flow="show",
    logy=True,
    style=style,
)

Variable bin widths, per-width normalisation and overflow bins, ATLAS style

import rootfig as rf

style = rf.Style(experiment="ATLAS", status="Internal", lumi=140, com=13.6)

edges = [0, 20, 40, 60, 80, 100, 130, 160, 200, 250, 320, 400]
rf.plot(
    mc,
    rf.Variable("MET", bins=edges, label=r"$E_T^{miss}$", unit="GeV"),
    observed=data,
    stack=True,
    ratio=True,
    normalize="width",
    flow="show",
    logy=True,
    style=style,
)

Variable bin widths, per-width normalisation and overflow bins, CMS style

import rootfig as rf

style = rf.Style(experiment="CMS", status="Preliminary", lumi=138, com=13.6)

edges = [0, 20, 40, 60, 80, 100, 130, 160, 200, 250, 320, 400]
rf.plot(
    mc,
    rf.Variable("MET", bins=edges, label=r"$E_T^{miss}$", unit="GeV"),
    observed=data,
    stack=True,
    ratio=True,
    normalize="width",
    flow="show",
    logy=True,
    style=style,
)

Variable bin widths, per-width normalisation and overflow bins, LHCb style

import rootfig as rf

style = rf.Style(experiment="LHCb", status="Preliminary", lumi=9, com=13.6)

edges = [0, 20, 40, 60, 80, 100, 130, 160, 200, 250, 320, 400]
rf.plot(
    mc,
    rf.Variable("MET", bins=edges, label=r"$E_T^{miss}$", unit="GeV"),
    observed=data,
    stack=True,
    ratio=True,
    normalize="width",
    flow="show",
    logy=True,
    style=style,
)

Variable bin widths, per-width normalisation and overflow bins, ALICE style

import rootfig as rf

style = rf.Style(experiment="ALICE", status="Preliminary")

edges = [0, 20, 40, 60, 80, 100, 130, 160, 200, 250, 320, 400]
rf.plot(
    mc,
    rf.Variable("MET", bins=edges, label=r"$E_T^{miss}$", unit="GeV"),
    observed=data,
    stack=True,
    ratio=True,
    normalize="width",
    flow="show",
    logy=True,
    style=style,
)

Variable bin widths, per-width normalisation and overflow bins, DUNE style

import rootfig as rf

style = rf.Style(experiment="DUNE", status="Preliminary")

edges = [0, 20, 40, 60, 80, 100, 130, 160, 200, 250, 320, 400]
rf.plot(
    mc,
    rf.Variable("MET", bins=edges, label=r"$E_T^{miss}$", unit="GeV"),
    observed=data,
    stack=True,
    ratio=True,
    normalize="width",
    flow="show",
    logy=True,
    style=style,
)

Setup

The code reads the toy dataset. In a checkout of rootfig, python examples/gallery writes it to examples/out/ in a few seconds; run the code inside that directory.

It uses these samples and variables, shared by the gallery examples (see Samples, variables, cuts and styles):

signal = rf.Sample("signal.root", tree="events", label="Signal", weight="weight", scale=0.03)
zjets = rf.Sample("background.root", tree="events", label="Z + jets", weight="weight")
diboson = rf.Sample("diboson.root", tree="events", label="Diboson", weight="weight", scale=0.15)
data = rf.Sample("data.root", tree="events", label="Data", is_data=True)
mc = [zjets, diboson, signal]  # stacked bottom to top

pt = rf.Variable("Muon_pt", bins=(30, 0, 300), label=r"$p_T^{\mu}$", unit="GeV")
mll = rf.Variable("m_ll", bins=(70, 50, 260), label=r"$m_{\ell\ell}$", unit="GeV")
met = rf.Variable("MET", bins=(40, 0, 400), label=r"$E_T^{miss}$", unit="GeV")