Skip to main content

A JAX-based gravitational-wave population inference

Project description

GWKokab

A JAX-based gravitational-wave population inference toolkit

GitHub License Python package

GWKokab is a JAX-based gravitational-wave population inference toolkit. It is designed to be a high-performance, flexible and easy-to-use library for sampling from a wide range of gravitational-wave population models. It is built on top of JAX, a high-performance numerical computing library, and is designed to be easily integrated into existing JAX workflows.

If you like to contribute, please see the contributing guidelines.

Citing GWKokab

If you use GWKokab in your research, please cite the following paper:

@software{gwkokab2024github,
    author = {Meesum Qazalbash, Muhammad Zeeshan, Richard O'Shaughnessy},
    title = {{GWKokab}: A JAX-based gravitational-wave population inference},
    url = {https://github.com/gwkokab/gwkokab},
    version = {0.0.1},
    year = {2024}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gwkokab-0.0.1.tar.gz (47.6 kB view details)

Uploaded Source

Built Distribution

gwkokab-0.0.1-py3-none-any.whl (77.8 kB view details)

Uploaded Python 3

File details

Details for the file gwkokab-0.0.1.tar.gz.

File metadata

  • Download URL: gwkokab-0.0.1.tar.gz
  • Upload date:
  • Size: 47.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.9

File hashes

Hashes for gwkokab-0.0.1.tar.gz
Algorithm Hash digest
SHA256 dedf8b5954a78ddb69d3ee6a16193c109c95d3ab00f71879c5a53eee8cfe3f75
MD5 0f3a0621db53725917bd3414dc826fff
BLAKE2b-256 7b03d2b07477941486f01af473611783f341f1ba2fa4fba7661dc02a16579143

See more details on using hashes here.

File details

Details for the file gwkokab-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: gwkokab-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 77.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.9

File hashes

Hashes for gwkokab-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 b26c358faff9dec1c67468b7703174a35b590a25038ce1bd39746f43bbb0a763
MD5 c66670199feddd987a5ea41f97fbdf44
BLAKE2b-256 ac4b7d95ef385e1ef672811310a4df2a5d213f4e5dfabc27dbe794aad413bd7d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page