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matplotlib-hep

An add-on for matplotlib that simplifies the creation of plots for high energy physics.

Getting started

This package is not yet on PyPI. You can install it from this repository by running

pip install --user git+https://github.com/ibab/matplotlib-hep

The histpoints plotting function

In high energy physics (HEP), histograms are often displayed as a collection of data points, one for each bin. This allows one to easily compare the data to an underlying probability model.

It can be debated if it is correct to attach error bars to the individual bin contents as opposed to the underlying model, as we want to know if our expectation for any given bin could fluctuate to match the data and not vice versa. But this is a convention widely used by the HEP community, and thus a way to use this kind of plot in Python is often necessary.

A simple way to create these kinds of plots is missing from other Python packages like matplotlib. The histpoints function is designed to produce these plots conventiently, like in the following example:

from matplotlib_hep import histpoints
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats

data = np.random.normal(0, 1, 200)
x, y, norm = histpoints(data)

xs = np.linspace(-4, 4, 200)
plt.plot(xs, norm * stats.norm.pdf(xs, 0, 1), 'b-', lw=2)

plt.savefig('histpoints.png')

Or, displaying horizontal error bars to mark the bin width:

histpoints(data, xerr='binwidth')

Note that the histpoints function returns the x and y coordinates of the points, as well as norm, the integral of the histogram. This can be used to scale a probability distribution to the histogram, as opposed to the other way around, like in the example above. This is often preferred in HEP, as it allows you to gauge the amount of entries in your data sample more easily.

By default, histpoints chooses the number of bins automatically via the Freedman-Diaconis rule.

The plot_pull function

from matplotlib_hep import plot_pull
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp

data = np.random.normal(0, 1, 1000)

func = lambda x: sp.stats.norm.pdf(x, 0, 1)

plot_pull(data, func)
plt.savefig('pull.png')