| Name | basil-core JSON |
| Version |
1.1.0
JSON |
| download |
| home_page | None |
| Summary | BASIL-CORE: Bayesian Analytic Sampling and Integrating Library - Compiled and low-dependency utilities |
| upload_time | 2025-11-10 19:36:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | MIT License |
| keywords |
gravitational wave
bayesian inference
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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# Basil Core
BASIL (Bayesian Analytic Sampling and Integrating Library) Core utilities;
Core utilities: A bunch of c functions that are faster than numpy matrix operations (and more conservative of your computer's RAM).
I am using this primarily in gravitational-wave population synthesis, for postprocessing with different binary evolution simulations.
User guide and jupyter notebooks incoming TBD.
## stats
`basil_core.stats.distance`
Right now, this includes a Bhattacharyya distance, Helinski distance, and relative entropy calculation
The relative entropy calculation has the advantage that it can accept pre-computed log values for P and Q.
## Astro
`basil_core.astro.coordinates` includes many coordinate transforms useful for GW astronomy, including chieff/chiminus transformations and tidal deformability parameters.
`basil_core.astro.orbit` includes many useful napkin calculations for GW astronomy, such as a timescale for a GW merger as a function of radius.
Many of these were adapted from [hush](https://github.com/katiebreivik/hush)
## Installation:
```
python3 -m pip install basil-core
```
## Contributing
We are open to pull requests.
If you would like to make a contribution, please explain what changs your are making and why.
## License
[MIT](https://choosealicense.come/licenses/mit)
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