Name | lotus-nlte JSON |
Version |
0.1.1
JSON |
| download |
home_page | https://github.com/Li-Yangyang/LOTUs |
Summary | Determine atmospheric stellar parameters in non-LTE |
upload_time | 2023-01-04 03:37:04 |
maintainer | Yangyang Li |
docs_url | None |
author | Yangyang Li |
requires_python | >= 3.7, < 3.8 |
license | MIT |
keywords |
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VCS |
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bugtrack_url |
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requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
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coveralls test coverage |
No coveralls.
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<p align="center">
<img width="20%" src="https://raw.githubusercontent.com/Li-Yangyang/LOTUS/main/doc/_static/logo.png">
<br><br>
<a href="http://lotus-nlte.readthedocs.io">
<img src="https://readthedocs.org/projects/lotus_nlte/badge/?version=latest" alt="Docs">
</a>
<a href="https://github.com/Li-Yangyang/LOTUS/blob/main/LICENSE">
<img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License">
</a>
<a href="https://arxiv.org/abs/2207.09415">
<img src="https://img.shields.io/badge/Arxiv-2207.09415-orange.svg" alt="Arxiv">
</a>
</p>
# LOTUS
_LOTUS_ (non-LTE Optimization Tool Utilized for the derivation of atmospheric
Stellar parameters) is a python package for the derivation of stellar parameters via _Equivalent Width (EW)_ method with the assumption of
**1D Non Local Thermodynamic Equilibrium**. It mainly applies on the spectroscopic
data from high resolution spectral survey. It can provide extremely accurate
measurement of stellar parameters compared with non-spectroscipic analysis from
benchmark stars.
Full documentation at [lotus-nlte.readthedocs.io](https://lotus-nlte.readthedocs.io)
## Installation
The quickest way to get started is to use [pip](https://pip.pypa.io):
```bash
python -m pip install lotus-nlte==0.1.1
```
Notice that _LOTUS_ requires Python 3.7.*. You might create an independent environment to run this code.
## Usage
Check out the user guides and tutorial docs on [the docs
page](https://lotus-nlte.readthedocs.io) for details.
## Contributing
_LOTUS_ is an open source code so if you would like to contribute your work please
report an issue or clone this repository to your local end to contribute any changes.
## Attribution
Our paper has been submitted to _The Astronomical Journal_ and is being peer-reviewed. We also post it on arxiv and we will update citation after being accepted. If you use _LOTUS_ in your research, please cite:
@ARTICLE{2022arXiv220709415L,
author = {{Li}, Yangyang and {Ezzeddine}, Rana},
title = "{LOTUS: A (non-)LTE Optimization Tool for Uniform derivation of Stellar atmospheric parameters}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2022,
month = jul,
eid = {arXiv:2207.09415},
pages = {arXiv:2207.09415},
archivePrefix = {arXiv},
eprint = {2207.09415},
primaryClass = {astro-ph.SR},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220709415L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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"description": "<p align=\"center\">\n <img width=\"20%\" src=\"https://raw.githubusercontent.com/Li-Yangyang/LOTUS/main/doc/_static/logo.png\">\n <br><br>\n <a href=\"http://lotus-nlte.readthedocs.io\">\n <img src=\"https://readthedocs.org/projects/lotus_nlte/badge/?version=latest\" alt=\"Docs\">\n </a>\n <a href=\"https://github.com/Li-Yangyang/LOTUS/blob/main/LICENSE\">\n <img src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" alt=\"License\">\n </a>\n <a href=\"https://arxiv.org/abs/2207.09415\">\n <img src=\"https://img.shields.io/badge/Arxiv-2207.09415-orange.svg\" alt=\"Arxiv\">\n </a>\n</p>\n\n# LOTUS\n_LOTUS_ (non-LTE Optimization Tool Utilized for the derivation of atmospheric\nStellar parameters) is a python package for the derivation of stellar parameters via _Equivalent Width (EW)_ method with the assumption of\n**1D Non Local Thermodynamic Equilibrium**. It mainly applies on the spectroscopic\ndata from high resolution spectral survey. It can provide extremely accurate\nmeasurement of stellar parameters compared with non-spectroscipic analysis from\nbenchmark stars.\n\nFull documentation at [lotus-nlte.readthedocs.io](https://lotus-nlte.readthedocs.io)\n\n## Installation\n\nThe quickest way to get started is to use [pip](https://pip.pypa.io):\n\n```bash\npython -m pip install lotus-nlte==0.1.1\n```\nNotice that _LOTUS_ requires Python 3.7.*. You might create an independent environment to run this code.\n\n## Usage\n\nCheck out the user guides and tutorial docs on [the docs\npage](https://lotus-nlte.readthedocs.io) for details.\n\n## Contributing\n\n_LOTUS_ is an open source code so if you would like to contribute your work please\nreport an issue or clone this repository to your local end to contribute any changes.\n\n## Attribution\n\nOur paper has been submitted to _The Astronomical Journal_ and is being peer-reviewed. We also post it on arxiv and we will update citation after being accepted. If you use _LOTUS_ in your research, please cite:\n\n\n @ARTICLE{2022arXiv220709415L,\n author = {{Li}, Yangyang and {Ezzeddine}, Rana},\n title = \"{LOTUS: A (non-)LTE Optimization Tool for Uniform derivation of Stellar atmospheric parameters}\",\n journal = {arXiv e-prints},\n keywords = {Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},\n year = 2022,\n month = jul,\n eid = {arXiv:2207.09415},\n pages = {arXiv:2207.09415},\n archivePrefix = {arXiv},\n eprint = {2207.09415},\n primaryClass = {astro-ph.SR},\n adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220709415L},\n adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n }\n\n\n",
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