Metadata-Version: 2.4
Name: primesw
Version: 2.0.0
Summary: Probabilistic Regressor for Input to the Magnetosphere Estimation (Solar Wind)
License: GPL-3.0
License-File: LICENSE
Author: Connor OBrien
Author-email: obrienco@bu.edu
Requires-Python: >=3.8,<=3.12
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Requires-Dist: cdasws (>=1.8.9,<2.0.0)
Requires-Dist: cdflib (>=1.3.2,<2.0.0)
Requires-Dist: lightning (>=2.5,<2.6)
Requires-Dist: loguru (<1.0.0)
Requires-Dist: matplotlib (>=3.4)
Requires-Dist: numpy (>=1.20.3,<2.0.0)
Requires-Dist: pandas (<3.0)
Requires-Dist: protobuf (==3.20.0)
Requires-Dist: scipy (>=1.6)
Requires-Dist: tables (<4.0.0)
Requires-Dist: xarray (>=2024.1.0,<2026.0.0)
Project-URL: Homepage, https://primesw.readthedocs.io/en/latest/
Description-Content-Type: text/markdown

# PRIME
[![DOI](https://zenodo.org/badge/648224321.svg)](https://zenodo.org/badge/latestdoi/648224321)

PRIME (Probabilistic Regressor for Input to the Magnetosphere Estimation) is a probabilistic algorithm that uses solar wind time history from L1 monitors to generate predictions of near-Earth solar wind with uncertainties. Install with
```
pip install primesw
```

For usage, read the [documentation](https://primesw.readthedocs.io/en/latest/).

For information on PRIME, including results from its validation and caveats on its use, please see the [paper](https://www.frontiersin.org/articles/10.3389/fspas.2023.1250779/full).

If you make use of PRIME, please cite it:
```
@article{obrien_prime_2023,
	title = {{PRIME}: a probabilistic neural network approach to solar wind propagation from {L1}},
	volume = {10},
	issn = {2296-987X},
	shorttitle = {{PRIME}},
	url = {https://www.frontiersin.org/articles/10.3389/fspas.2023.1250779/full},
	doi = {10.3389/fspas.2023.1250779},
	urldate = {2023-11-13},
	journal = {Frontiers in Astronomy and Space Sciences},
	author = {O’Brien, Connor and Walsh, Brian M. and Zou, Ying and Tasnim, Samira and Zhang, Huaming and Sibeck, David Gary},
	month = sep,
	year = {2023},
	pages = {1250779},
}
```
