Variable vs efficiency plots

In the following a small example how to plot the efficiency vs a specific variable. In this case, we use pt as this variable.

To set up the inputs for the plots, have a look here.

Then we can start the actual plotting part

"""Produce pT vs efficiency plot from tagger output and labels."""

from __future__ import annotations

import numpy as np
from ftag import get_discriminant

from puma import VarVsEff, VarVsEffPlot
from puma.utils import get_dummy_2_taggers, logger

# The line below generates dummy data which is similar to a NN output
df = get_dummy_2_taggers(add_pt=True)

logger.info("caclulate tagger discriminants")
discs_rnnip = get_discriminant(df, "rnnip", signal="bjets", fc=0.018)
discs_dips = get_discriminant(df, "dips", signal="bjets", fc=0.018)

# you can also use a results file directly, you can comment everything above and
# uncomment below
# ttbar_file = "<resultsfile.h5"
# df = pd.read_hdf(ttbar_file, key="ttbar")

# discs_rnnip = df["disc_rnnip"]
# discs_dips = df["disc_dips"]
# is_light = df["labels"] == 0
# is_c = df["labels"] == 1
# is_b = df["labels"] == 2

# Getting jet pt in GeV
pt = df["pt"] / 1e3
# defining target efficiency
sig_eff = np.linspace(0.49, 1, 20)
# defining boolean arrays to select the different flavour classes
is_light = df["HadronConeExclTruthLabelID"] == 0
is_c = df["HadronConeExclTruthLabelID"] == 4
is_b = df["HadronConeExclTruthLabelID"] == 5

# here the plotting starts

# define the curves
rnnip_light = VarVsEff(
    x_var_sig=pt[is_b],
    disc_sig=discs_rnnip[is_b],
    x_var_bkg=pt[is_light],
    disc_bkg=discs_rnnip[is_light],
    bins=[20, 30, 40, 60, 85, 110, 140, 175, 250],
    working_point=0.7,
    disc_cut=None,
    flat_per_bin=False,
    label="RNNIP",
)
dips_light = VarVsEff(
    x_var_sig=pt[is_b],
    disc_sig=discs_dips[is_b],
    x_var_bkg=pt[is_light],
    disc_bkg=discs_dips[is_light],
    bins=[20, 30, 40, 60, 85, 110, 140, 175, 250],
    working_point=0.7,
    disc_cut=None,
    flat_per_bin=False,
    label="DIPS",
)


logger.info("Plotting bkg rejection for inclusive efficiency as a function of pt.")
# You can choose between different modes: "sig_eff", "bkg_eff", "sig_rej", "bkg_rej"
plot_bkg_rej = VarVsEffPlot(
    mode="bkg_rej",
    ylabel="Light-flavour jets rejection",
    xlabel=r"$p_{T}$ [GeV]",
    logy=False,
    atlas_second_tag="$\\sqrt{s}=13$ TeV, dummy jets \ndummy sample, $f_{c}=0.018$",
    n_ratio_panels=1,
)
plot_bkg_rej.add(rnnip_light, reference=True)
plot_bkg_rej.add(dips_light)

plot_bkg_rej.draw()
plot_bkg_rej.savefig("pt_light_rej.png")

plot_sig_eff = VarVsEffPlot(
    mode="sig_eff",
    ylabel="$b$-jets efficiency",
    xlabel=r"$p_{T}$ [GeV]",
    logy=False,
    atlas_second_tag="$\\sqrt{s}=13$ TeV, dummy jets, \ndummy sample, $f_{c}=0.018$",
    n_ratio_panels=1,
)
plot_sig_eff.add(rnnip_light, reference=True)
plot_sig_eff.add(dips_light)

plot_sig_eff.atlas_second_tag += "\nInclusive $\\epsilon_b=70%%$"

# If you want to inverse the discriminant cut you can enable it via
# plot_sig_eff.set_inverse_cut()
plot_sig_eff.draw()
# Drawing a hline indicating inclusive efficiency
plot_sig_eff.draw_hline(0.7)
plot_sig_eff.savefig("pt_b_eff.png", transparent=False)