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

Area of Science:

• Computational chemistry
• Data visualization
• Scientific software development

Background:

• Molecular simulation software like GROMACS generates extensive time-series data.
• Creating publication-quality figures from this data typically requires commercial software or custom scripting.
• The increasing volume of data necessitates efficient and reproducible visualization methods.

Purpose of the Study:

• To introduce plotXVG, a user-friendly Python tool for generating publication-quality graphics.
• To provide a solution for rapid and reproducible data visualization from molecular simulations and other sources.
• To offer flexibility for both non-programmers and those integrating visualization into machine learning workflows.

Main Methods:

• Utilized the Matplotlib plotting library in Python.
• Developed a simple tool, plotXVG, with an accessible interface.
• Enabled generation of line graphs, heatmaps, and contour plots.
• Provided an optional application programming interface (API) for advanced integration.

Main Results:

• plotXVG generates publication-quality graphics for various plot types.
• The tool facilitates rapid and reproducible creation of figure files.
• It supports data from molecular simulations and other scientific domains.
• The software is free and open-source, allowing user customization.

Conclusions:

• plotXVG offers an efficient, reproducible, and accessible method for scientific data visualization.
• The tool democratizes the creation of high-quality figures, supporting both manuscript preparation and computational research.
• Its open-source nature encourages community contribution and adaptation.