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A correlation matrix

rf.correlation computes the (weighted) linear correlation of several per-event quantities and draws it as an annotated matrix; percent=True labels cells in percent.

A correlation matrix, Default style A correlation matrix, Default style

import rootfig as rf

style = rf.Style()

rf.correlation(
    signal,
    ["MET", "sum(Jet_pt)", "nJet", "nMuon", "m_ll"],
    labels=[r"$E_T^{miss}$", r"$H_T$", r"$N_{jet}$", r"$N_{\mu}$", r"$m_{\ell\ell}$"],
    percent=True,
    style=style,
)

A correlation matrix, ATLAS style A correlation matrix, ATLAS style

import rootfig as rf

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

rf.correlation(
    signal,
    ["MET", "sum(Jet_pt)", "nJet", "nMuon", "m_ll"],
    labels=[r"$E_T^{miss}$", r"$H_T$", r"$N_{jet}$", r"$N_{\mu}$", r"$m_{\ell\ell}$"],
    percent=True,
    style=style,
)

A correlation matrix, CMS style A correlation matrix, CMS style

import rootfig as rf

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

rf.correlation(
    signal,
    ["MET", "sum(Jet_pt)", "nJet", "nMuon", "m_ll"],
    labels=[r"$E_T^{miss}$", r"$H_T$", r"$N_{jet}$", r"$N_{\mu}$", r"$m_{\ell\ell}$"],
    percent=True,
    style=style,
)

A correlation matrix, LHCb style A correlation matrix, LHCb style

import rootfig as rf

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

rf.correlation(
    signal,
    ["MET", "sum(Jet_pt)", "nJet", "nMuon", "m_ll"],
    labels=[r"$E_T^{miss}$", r"$H_T$", r"$N_{jet}$", r"$N_{\mu}$", r"$m_{\ell\ell}$"],
    percent=True,
    style=style,
)

A correlation matrix, ALICE style A correlation matrix, ALICE style

import rootfig as rf

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

rf.correlation(
    signal,
    ["MET", "sum(Jet_pt)", "nJet", "nMuon", "m_ll"],
    labels=[r"$E_T^{miss}$", r"$H_T$", r"$N_{jet}$", r"$N_{\mu}$", r"$m_{\ell\ell}$"],
    percent=True,
    style=style,
)

A correlation matrix, DUNE style A correlation matrix, DUNE style

import rootfig as rf

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

rf.correlation(
    signal,
    ["MET", "sum(Jet_pt)", "nJet", "nMuon", "m_ll"],
    labels=[r"$E_T^{miss}$", r"$H_T$", r"$N_{jet}$", r"$N_{\mu}$", r"$m_{\ell\ell}$"],
    percent=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")