Unstacked

Imports

import hist
import numpy as np
import seaborn as sns

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, 3000),
            np.random.normal(-3, 0.8, 1500),
            np.random.normal(-2, 1.5, 1200),
            np.random.normal(0, 0.5, 500),
        ]
    )
)

# Create background histograms
background_hists = [
    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 2, 3500)),
    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-3, 0.8, 1800)),
    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-2, 1.5, 1400)),
]

# Scale backgrounds to match data
scale = data_hist.sum().value / sum(background_hists).sum().value
background_hists = [scale * h for h in background_hists]

Code

fig, ax_main, ax_comparison = mh.comp.data_model(
    data_hist=data_hist,
    unstacked_components=background_hists,
    unstacked_labels=["c0", "c1", "c2"],
    unstacked_colors=sns.color_palette("cubehelix", 3),
    xlabel="Observable",
    ylabel="Entries",
    model_sum_kwargs={"label": "Sum(hists)", "color": "navy"},
    comparison_ylim=[0.5, 1.5],
)

Full code

import hist
import numpy as np
import seaborn as sns

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, 3000),
            np.random.normal(-3, 0.8, 1500),
            np.random.normal(-2, 1.5, 1200),
            np.random.normal(0, 0.5, 500),
        ]
    )
)

# Create background histograms
background_hists = [
    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 2, 3500)),
    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-3, 0.8, 1800)),
    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-2, 1.5, 1400)),
]

# Scale backgrounds to match data
scale = data_hist.sum().value / sum(background_hists).sum().value
background_hists = [scale * h for h in background_hists]

fig, ax_main, ax_comparison = mh.comp.data_model(
    data_hist=data_hist,
    unstacked_components=background_hists,
    unstacked_labels=["c0", "c1", "c2"],
    unstacked_colors=sns.color_palette("cubehelix", 3),
    xlabel="Observable",
    ylabel="Entries",
    model_sum_kwargs={"label": "Sum(hists)", "color": "navy"},
    comparison_ylim=[0.5, 1.5],
)