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This section covers advanced mplhep features and utilities..

Prerequisites

Throughout this guide the following codeblock is assumed.

import matplotlib.pyplot as plt
import numpy as np
import hist

np.random.seed(42)
import mplhep as mh
# mh.style.use('<as appropriate>')

Working with UHI Histograms

mplhep fully supports the Unified Histogram Interface, making it compatible with modern histogram libraries:

import hist
import boost_histogram as bh

# Using hist library
h = hist.Hist(hist.axis.Regular(50, -3, 3, name="x", label="Observable [GeV]"))
h.fill(data)
mh.histplot(h)  # Automatically uses axis labels

# Using boost_histogram
h_boost = bh.Histogram(bh.axis.Regular(50, -3, 3))
h_boost.fill(data)
mh.histplot(h_boost)

# From ROOT files via uproot
import uproot

file = uproot.open("data.root")
h_root = file["histogram_name"]
mh.histplot(h_root)

Best Practices

Consistent Binning

When plotting multiple histograms together, ensure they share the same binning:

# Define bins once
bins = np.linspace(-4, 4, 41)

# Use same bins for all histograms
h1 = np.histogram(data1, bins=bins)
h2 = np.histogram(data2, bins=bins)

mh.histplot([h1[0], h2[0]], bins=bins)

Error Representation

Always show uncertainties for data:

# For data points
mh.histplot(data_hist, bins=bins, yerr=True, histtype="errorbar", label="Data")

# For Monte Carlo, consider filled uncertainty bands
mh.histplot(mc_hist, bins=bins, histtype="fill", alpha=0.3, label="MC")

Axis Labels with Units

Include units in axis labels following HEP conventions:

ax.set_xlabel("Mass [GeV]")
ax.set_ylabel("Events / 5 GeV")  # Include bin width

Using Legends

Place legends appropriately and use clear labels:

mh.histplot([h1, h2], bins=bins, label=["Signal", "Background"])
ax.legend(loc="upper right", frameon=False)

Style Consistency

Apply styles at the beginning of your script, not in functions:

# At the top of your script
import mplhep as mh

mh.style.use("CMS")

# Then create all plots
# ...