Jun 01, 2022
MDAnalysis (www.mdanalysis.org) is an object-oriented python toolkit to analyze molecular dynamics trajectories generated by CHARMM, Gromacs, Amber, NAMD, LAMMPS, DL_POLY and other packages; it also reads other formats (e.g., PDB files and XYZ format trajectories; see Table of supported coordinate formats and Table of Supported Topology Formats for the full lists). It can write most of these formats, too, together with atom selections for use in Gromacs, CHARMM, VMD and PyMol (see Selection exporters).
It allows one to read molecular dynamics trajectories and access the atomic coordinates through NumPy arrays. This provides a flexible and relatively fast framework for complex analysis tasks. Fairly complete atom Selection commands are implemented. Trajectories can also be manipulated (for instance, fit to a reference structure) and written out in a range of formats.
The MDAnalysis community subscribes to a Code of Conduct that all community members agree and adhere to — please read it.
The MDAnalysis User Guide provides comprehensive information on how to use the library. We would recommend that new users have a look at the Quick Start Guide. The User Guide also has a set of examples on how to use the MDAnalysis library which may be of interest.
First installation with conda:
conda config --add channels conda-forge conda install mdanalysis
which will automatically install a full set of dependencies.
To upgrade later:
conda update mdanalysis
Installation with pip and a minimal set of dependencies:
pip install --upgrade MDAnalysis
To install with a full set of dependencies (which includes everything needed
MDAnalysis.analysis), add the
pip install --upgrade MDAnalysis[analysis]
conda install mdanalysistests # with conda pip install --upgrade MDAnalysisTests # with pip
git clone https://github.com/MDAnalysis/mdanalysis.git
MDAnalysis also contains specific algorithms and whole analysis modules whose algorithms have also been published in the scientific literature. Please make sure to also reference any Citations for included algorithms and modules in published work.