swiftsimio

SWIFTsimIO

The SWIFT astrophysical simulation code (http://swift.dur.ac.uk) is used widely. There exists many ways of reading the data from SWIFT, which outputs HDF5 files. These range from reading directly using h5py to using a complex system such as yt; however these either are unsatisfactory (e.g. a lack of unit information in reading HDF5), or too complex for most use-cases. swiftsimio provides an object-oriented API to read (dynamically) data from SWIFT.

Full documentation is available at ReadTheDocs.

Getting set up with swiftsimio is easy; it (by design) has very few requirements. There are a number of optional packages that you can install to make the experience better and these are recommended.

Requirements

This requires python v3.8.0 or higher. Unfortunately it is not possible to support swiftsimio on versions of python lower than this. It is important that you upgrade if you are still a python2 user.

Python packages

• numpy, required for the core numerical routines.
• h5py, required to read data from the SWIFT HDF5 output files.
• unyt, required for symbolic unit calculations (depends on sympy`).

Optional packages

• numba, highly recommended should you wish to use the in-built visualisation tools.
• scipy, required if you wish to generate smoothing lengths for particle types that do not store this variable in the snapshots (e.g. dark matter)
• tqdm, required for progress bars for some long-running tasks. If not installed no progress bar will be shown.
• py-sphviewer, if you wish to use our integration with this visualisation code.

Installing

swiftsimio can be installed using the python packaging manager, pip, or any other packaging manager that you wish to use:

pip install swiftsimio

Citing

Please cite swiftsimio using the JOSS paper:

@article{Borrow2020,
  doi = {10.21105/joss.02430},
  url = {https://doi.org/10.21105/joss.02430},
  year = {2020},
  publisher = {The Open Journal},
  volume = {5},
  number = {52},
  pages = {2430},
  author = {Josh Borrow and Alexei Borrisov},
  title = {swiftsimio: A Python library for reading SWIFT data},
  journal = {Journal of Open Source Software}
}

If you use any of the subsampled projection backends, we ask that you cite our relevant SPHERIC paper. Note that citing the arXiv version here is recommended as the ADS cannot track conference proceedings well.

@article{Borrow2021
  title={Projecting SPH Particles in Adaptive Environments}, 
  author={Josh Borrow and Ashley J. Kelly},
  year={2021},
  eprint={2106.05281},
  archivePrefix={arXiv},
  primaryClass={astro-ph.GA}
}