A simple, fast and cross-platform python library to handle the data generated from molecular dynamics simulations.
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The mdapy python library provides an array of powerful, flexible, and straightforward
tools to analyze atomic trajectories generated from Molecular Dynamics (MD) simulations. The library is fully
cross-platform, making it accessible to users in Windows, Linux, and Mac OS.
Benefited by the TaiChi <https://github.com/taichi-dev/taichi>_ project,
we can effectively accelerate the pure python code, bringing it closer to the speed of code written in C++.
Furthermore, mdapy is highly parallelized, allowing users to leverage the resources of both multicore CPU and GPU.
mdapy can directly handle the DUMP and DATA formats in LAMMPS <https://www.lammps.org/>, POSCAR format in VASP <https://www.vasp.at/wiki/index.php/The_VASP_Manual>,
universal XYZ format and CIF format. Besides, all data in mdapy is stored in NDARRAY format in NumPy <https://numpy.org/>, which enables easy integration
with the scientific ecosystem in python and facilitates collaboration with other post-progressing
tools such as OVITO <https://www.ovito.org/> and freud <https://github.com/glotzerlab/freud>_.
https://github.com/mushroomfire/mdapy <https://github.com/mushroomfire/mdapy>_https://mdapy.readthedocs.io/ <https://mdapy.readthedocs.io/>_https://github.com/mushroomfire/mdapy/issues <https://github.com/mushroomfire/mdapy/issues>_FAQs
A simple, fast and cross-platform python library to handle the data generated from molecular dynamics simulations.
We found that mdapy demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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