Metadata-Version: 2.1
Name: gwalk
Version: 4.1.0
Summary: GWALK: Gravitational Wave Approximate LiKelihood
Keywords: Gravitational Wave,Bayesian Inference
Author-Email: "V. Delfavero" <xevra86@gmail.com>
Maintainer-Email: "V. Delfavero" <xevra86@gmail.com>
License: MIT License
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Project-URL: Homepage, https://gitlab.com/xevra/gwalk
Project-URL: Bug tracker, https://gitlab.com/xevra/gwalk/issues
Requires-Python: >=3.12
Requires-Dist: numpy<2.4.0,>=1.21.6
Requires-Dist: matplotlib>=2.2.4
Requires-Dist: scipy>=1.5.0
Requires-Dist: h5py>=2.10.0
Requires-Dist: astropy>=4.0
Requires-Dist: basil-core>=1.2.2
Requires-Dist: csrk==0.1.4
Requires-Dist: xev-data>=0.1.1
Description-Content-Type: text/markdown

# 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: <a href=https://gitlab.com/xevra/nal-data
    target="_blank">nal-data</a>.

See also <a href=https://gitlab.com/xevra/basil-core target="_blank">basil-core</a>,
    <a href=https://gitlab.com/xevra/csrk-py target="_blank">csrk-py</a>.

## 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 may only work with python 3.12 or newer, and requires the Rust compiler cargo to be installed on a computer.
```
python3 -m pip install gwalk
```

Method 2:

This should work on any computer with anaconda:
```
conda create --name gwalk python=3.12
conda activate gwalk
conda install numpy==2.3.5
conda install h5py
conda install rust
pip install gwalk
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)
