Interactive Data Visualizer

Drag and drop massive multi-column data logs. The parser natively ignores GROMACS metadata and renders standard CSVs instantly using WebGL.

Supported formats: .xvg, .csv, .txt, .dat

Guide

How to visualize your data

Drop a GROMACS .xvg (or any whitespace/CSV numeric table) and explore every column interactively, the parser reads the embedded metadata so your axes and legends come out labelled.

1

Load a file

Drag & drop or select a .xvg, .csv, .txt or .dat file, or click load a sample to try a synthetic RMSD/Rg trace. Everything is parsed in your browser.

2

Pick columns

Choose which column is the X axis, then tick any number of Y columns to overlay. Legends and axis titles are pulled from the file's @ metadata when present.

3

Adjust the view

Toggle a log Y axis, show markers, or apply spline smoothing. The plot is fully zoomable and pannable via the Plotly toolbar.

4

Export or reproduce

Use the camera icon to save a PNG, or click Python to copy matplotlib code that redraws the current selection from your file with numpy.loadtxt.

Format

About the GROMACS .xvg format

.xvg is the plain-text output written by GROMACS analysis tools (and read by Grace/xmgrace). Knowing its structure explains what this tool does automatically.

Comment & metadata lines

Lines starting with # are comments; lines starting with @ are Grace formatting commands. This tool reads @ title, @ xaxis label, @ yaxis label and @ sN legend to label the plot, and skips everything else, so the raw numeric matrix is all that gets plotted.

The data block

Below the metadata is a whitespace-separated numeric table: the first column is usually the independent variable (time, distance, reaction coordinate) and each remaining column a data series. Common outputs include RMSD (gmx rms), radius of gyration (gmx gyrate), and PMF/free-energy profiles.

Reproducing in Python

The Python export uses numpy.loadtxt(..., comments=['@', '#']), which is the idiomatic way to read an .xvg: the comments argument makes NumPy skip exactly the metadata lines described above, leaving the numeric matrix. The generated script plots only the columns you selected.

References

References & documentation

  1. Abraham, M. J., Murtola, T., Schulz, R., et al. (2015). GROMACS: High performance molecular simulations through multi-level parallelism. SoftwareX, 1–2, 19–25. GROMACS file formats.
  2. Turner, P. J. (2005). XMGRACE (Grace 5.1). Center for Coastal and Land-Margin Research, OGI. (origin of the .xvg/@-command format)
  3. Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585, 357–362. numpy.loadtxt.
  4. Software: Plotly.js (interactive WebGL rendering). Exported code targets matplotlib + NumPy.