plotXVG: Batch Generation of Publication-Quality Graphs from GROMACS Output

TL;DR

plotXVG is a Python tool that helps scientists quickly create high-quality graphs from simulation data, especially useful for publishing research.

Contribution

plotXVG introduces a user-friendly, open-source Python tool for generating publication-quality plots from simulation data.

Findings

plotXVG uses Matplotlib to generate line graphs, heatmaps, and contour plots from GROMACS output.

The tool supports rapid and reproducible generation of graphics without requiring advanced programming skills.

plotXVG is freely available and extensible for custom use cases beyond molecular simulations.

Abstract

Molecular simulation
tools, such as GROMACS, are used
routinely
to produce time series of energies and other observables. To turn
these data into publication-quality figures, a user can either use
a (commercial) software package with a graphical user interface, often
offering fine control and high-quality output, or write their own
code to make plots using a scripting language. In the age of big data
and machine learning, it is often necessary to generate many graphs,
be able to rapidly inspect them, and make plots for manuscripts. Here,
we provide a simple Python tool, plotXVG, built
on the well-known Matplotlib plotting library, that will generate
publication-quality graphics for line graphs as well as heatmaps and
contour plots. This will allow users to rapidly and reproducibly generate
a series of graphics files without programming, but a simple application
programming interface is available as well for incorporation in, e.g.,
machine learning applications. Obviously, the tool is applicable to
any kind of line graph data or heatmap, not just that from molecular
simulations. plotXVG is available as free and open source, which implies
that users can extend the tool to their own needs.

Figures