# Gravitational Wave Approximate LiKelihood (GWALK)
Library for fitting approximate likelihood functions for Gravitational Wave
events, with methods applicable in general for
modeling sample-based distributions.
Specifically, the Normal Approximate Likelihood (NAL) models
are optimized, bounded (truncated) multivariate normal distributions.
The non-parametric methods included also include density estimation
as marginalized Gaussian process estimates.
See the associated data release: https://gitlab.com/xevra/nal-data
See gp-api: https://gitlab.com/xevra/gaussian-process-api
## Citation
```
@misc{https://doi.org/10.48550/arxiv.2205.14154,
doi = {10.48550/ARXIV.2205.14154},
url = {https://arxiv.org/abs/2205.14154},
author = {Delfavero, Vera and O'Shaughnessy, Richard and Wysocki, Daniel and Yelikar, Anjali},
keywords = {Instrumentation and Methods for Astrophysics (astro-ph.IM), General Relativity and Quantum Cosmology (gr-qc), FOS: Physical sciences, FOS: Physical sciences},
title = {Compressed Parametric and Non-Parametric Approximations to the Gravitational Wave Likelihood},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
## Installation:
Method 1:
This will only work with python 3.7-3.9 (newer versions are waiting on cython version to update), and on a computer with cholmod installed (suitesparse, libsuitesparse-dev, etc...).
```
python3 -m pip install gwalk
```
Method 2:
This should work on any computer with anaconda:
```
conda create --name gwalk python=3.9
conda activate gwalk
conda install -c conda-forge scikit-sparse
python3 -m pip install gaussian-process-api
python3 -m pip install --upgrade ipykernel
python3 -m ipykernel install --user --name "gwalk" --display-name "gwalk" # For jupyter
```
## Contributing
We are open to pull requests.
If you would like to make a contribution, please explain what changes you are making and why.
## License
[MIT](https://choosealicense.com/licenses/mit)
Raw data
{
"_id": null,
"home_page": null,
"name": "gwalk",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.12",
"maintainer_email": "\"V. Delfavero\" <xevra86@gmail.com>",
"keywords": "Gravitational Wave, Bayesian Inference",
"author": null,
"author_email": "\"V. Delfavero\" <xevra86@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/a0/43/9c23856ed882923e12afd31cfb71245f4a6328ccbb28b3d994b6ded2f06e/gwalk-3.0.0.tar.gz",
"platform": null,
"description": "# Gravitational Wave Approximate LiKelihood (GWALK)\n\nLibrary for fitting approximate likelihood functions for Gravitational Wave\n events, with methods applicable in general for \n modeling sample-based distributions.\n\nSpecifically, the Normal Approximate Likelihood (NAL) models\n are optimized, bounded (truncated) multivariate normal distributions.\n\nThe non-parametric methods included also include density estimation\n as marginalized Gaussian process estimates.\n\nSee the associated data release: https://gitlab.com/xevra/nal-data\n\nSee gp-api: https://gitlab.com/xevra/gaussian-process-api\n\n## Citation\n```\n@misc{https://doi.org/10.48550/arxiv.2205.14154,\n doi = {10.48550/ARXIV.2205.14154},\n url = {https://arxiv.org/abs/2205.14154},\n author = {Delfavero, Vera and O'Shaughnessy, Richard and Wysocki, Daniel and Yelikar, Anjali},\n keywords = {Instrumentation and Methods for Astrophysics (astro-ph.IM), General Relativity and Quantum Cosmology (gr-qc), FOS: Physical sciences, FOS: Physical sciences},\n title = {Compressed Parametric and Non-Parametric Approximations to the Gravitational Wave Likelihood},\n publisher = {arXiv},\n year = {2022},\n copyright = {arXiv.org perpetual, non-exclusive license}\n}\n```\n\n## Installation:\n\nMethod 1:\n\nThis will only work with python 3.7-3.9 (newer versions are waiting on cython version to update), and on a computer with cholmod installed (suitesparse, libsuitesparse-dev, etc...).\n```\npython3 -m pip install gwalk\n```\n\nMethod 2:\n\nThis should work on any computer with anaconda:\n```\nconda create --name gwalk python=3.9\nconda activate gwalk\nconda install -c conda-forge scikit-sparse\npython3 -m pip install gaussian-process-api\npython3 -m pip install --upgrade ipykernel\npython3 -m ipykernel install --user --name \"gwalk\" --display-name \"gwalk\" # For jupyter \n```\n\n\n## Contributing\n\nWe are open to pull requests. \n\nIf you would like to make a contribution, please explain what changes you are making and why.\n\n## License\n\n[MIT](https://choosealicense.com/licenses/mit)\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "GWALK: Gravitational Wave Approximate LiKelihood",
"version": "3.0.0",
"project_urls": {
"Bug tracker": "https://gitlab.com/xevra/gwalk/issues",
"Homepage": "https://gitlab.com/xevra/gwalk"
},
"split_keywords": [
"gravitational wave",
" bayesian inference"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "a0439c23856ed882923e12afd31cfb71245f4a6328ccbb28b3d994b6ded2f06e",
"md5": "f39ac8e613470d57b6ed80f65c284162",
"sha256": "381b5ee1d4bb1faa8bae87cdc3e7e95b6d60328a114a47610b62b3c80275f438"
},
"downloads": -1,
"filename": "gwalk-3.0.0.tar.gz",
"has_sig": false,
"md5_digest": "f39ac8e613470d57b6ed80f65c284162",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.12",
"size": 4536271,
"upload_time": "2025-08-21T00:25:41",
"upload_time_iso_8601": "2025-08-21T00:25:41.366667Z",
"url": "https://files.pythonhosted.org/packages/a0/43/9c23856ed882923e12afd31cfb71245f4a6328ccbb28b3d994b6ded2f06e/gwalk-3.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-21 00:25:41",
"github": false,
"gitlab": true,
"bitbucket": false,
"codeberg": false,
"gitlab_user": "xevra",
"gitlab_project": "gwalk",
"lcname": "gwalk"
}
JSON