Over the last months, I’ve developed two Python packages: atlasify and nnfwtbn. The former is a package which applies the ATLAS collaboration specific plotting style to matplotlib plots, while the latter is a framework to train neural network tailored for high-energy physics based on keras. Both frameworks have a special focus on plotting. While the demand and the code base of two projects grew rapidly, I ran into two independent issues with matplotlib. At first, I assumed there is an issue in the way I use matplotlib; however, it turned out to be a bug in matplotlib itself.

I’ve submitted a pull request for both issues. The current master version of matplotlib contains both fixes. This article briefly describes the second of these issues and is part 2 of this series. The other bug was detailed in another article.

In high energy physics, the most common way to present data is in the form of a histogram, especially stacked histograms. In many cases, the data is already histogrammed, so I have to use the bottom and weights argument of matplotlib’s hist method. Consider the following snippet.

import matplotlib.pyplot as plt

entries = [2, 3]

# Bin edges
bins = [0, 1, 2]

         bottom=[1, 2],  # Set lower edge


The usage might look a bit weird, but this is a convenient way to achieve this particular task. The output, however, looks as follows:

Histogram which wrong outline

The first bin doesn’t look right. The bottom outline of the first bin should be horizontal and not be connected to the origin. As it turned out this was a bug in matplotlib. Version 3.3 will contain an updated version, which properly renders the histogram.