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Related Concept Videos

pV-Diagrams01:18

pV-Diagrams

The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line represents the process mean,...

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

Måns K Rosenbaum1, David van der Spoel1

  • 1Department of Cell and Molecular Biology, Uppsala University, Uppsala SE-75124, Sweden.

Journal of Chemical Information and Modeling
|March 10, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed plotXVG, a free Python tool for creating publication-quality figures from molecular simulation data. This tool simplifies generating line graphs, heatmaps, and contour plots for manuscripts and machine learning applications.

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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.