Ratio Data vs Model with Stacked and Unstacked Function Components

Imports

import hist
import numpy as np
from scipy.stats import norm

import mplhep as mh

np.random.seed(42)

Setup

# Create data histogram
data_hist = hist.new.Regular(50, -8, 8).Weight()
data_hist.fill(
    np.concatenate(
        [
            np.random.normal(0, 2, 3500),
            np.random.normal(-3, 1, 2000),
            np.random.normal(5, 0.5, 200),
        ]
    )
)
_binwidth = data_hist.axes[0].widths[0]


# Define model function components
def f_signal(x):
    return 200 * _binwidth * norm.pdf(x, loc=5, scale=0.5)


def f_background1(x):
    return 3500 * _binwidth * norm.pdf(x, loc=0, scale=2)


def f_background2(x):
    return 2000 * _binwidth * norm.pdf(x, loc=-3, scale=1)

Code

fig, ax_main, ax_comparison = mh.comp.data_model(
    data_hist=data_hist,
    stacked_components=[f_background1, f_background2],
    stacked_labels=["c0", "c1"],
    unstacked_components=[f_signal],
    unstacked_labels=["Signal"],
    unstacked_colors=["#8EBA42"],
    xlabel="Observable",
    ylabel="Entries",
    model_sum_kwargs={"show": True, "label": "Model", "color": "navy"},
    comparison="pull",
)

Full code

import hist
import numpy as np
from scipy.stats import norm

import mplhep as mh

np.random.seed(42)


# Create data histogram
data_hist = hist.new.Regular(50, -8, 8).Weight()
data_hist.fill(
    np.concatenate(
        [
            np.random.normal(0, 2, 3500),
            np.random.normal(-3, 1, 2000),
            np.random.normal(5, 0.5, 200),
        ]
    )
)
_binwidth = data_hist.axes[0].widths[0]


# Define model function components
def f_signal(x):
    return 200 * _binwidth * norm.pdf(x, loc=5, scale=0.5)


def f_background1(x):
    return 3500 * _binwidth * norm.pdf(x, loc=0, scale=2)


def f_background2(x):
    return 2000 * _binwidth * norm.pdf(x, loc=-3, scale=1)



fig, ax_main, ax_comparison = mh.comp.data_model(
    data_hist=data_hist,
    stacked_components=[f_background1, f_background2],
    stacked_labels=["c0", "c1"],
    unstacked_components=[f_signal],
    unstacked_labels=["Signal"],
    unstacked_colors=["#8EBA42"],
    xlabel="Observable",
    ylabel="Entries",
    model_sum_kwargs={"show": True, "label": "Model", "color": "navy"},
    comparison="pull",
)