scida is an out-of-the-box analysis tool for large scientific datasets. It primarily supports the astrophysics community, focusing on cosmological and galaxy formation simulations using particles or unstructured meshes, as well as large observational datasets. This tool uses dask, allowing analysis to scale up from your personal computer to HPC resources and the cloud.
- Unified, high-level interface to load and analyze large datasets from a variety of sources.
- Parallel, task-based data processing with dask arrays.
- Physical unit support via pint.
- Easily extensible architecture.
- Python 3.9, 3.10, 3.11
The documentation can be found here.
pip install scida
After installing scida, follow the tutorial.
If you use scida in your research, please cite the following paper:
`Byrohl et al., (2024). scida: scalable analysis for scientific big data. Journal of Open Source Software, 9(94), 6064, https://doi.org/10.21105/joss.06064`
with the following bibtex entry:
@article{scida,
title = {scida: scalable analysis for scientific big data},
author = {Chris Byrohl and Dylan Nelson},
doi = {10.21105/joss.06064},
url = {https://doi.org/10.21105/joss.06064}, year = {2024},
publisher = {The Open Journal}, volume = {9}, number = {94},
pages = {6064},
journal = {Journal of Open Source Software}
}
If you encounter any problems, please file an issue along with a detailed description.
Distributed under the terms of the MIT license, scida is free and open source software.