asimov-bayeswave 0.1.0

BayesWave pipeline integration for Asimov

asimov-bayeswave

BayesWave pipeline integration for Asimov.

This package provides a plugin for Asimov 0.7+ that enables integration with the BayesWave parameter estimation pipeline for gravitational wave data analysis.

Features

• 🔌 Plugin Architecture: Seamlessly integrates with Asimov via entry points
• 📊 PSD Generation: Automatic power spectral density estimation and collection
• 🔄 Format Conversion: Converts PSDs to XML format for use with other pipelines
• 🚀 HTCondor Integration: Automated DAG generation and job submission
• 📈 Result Collection: Automatic collection of megaplot outputs and visualizations
• 🎯 PSD Suppression: Support for suppressing frequency bands in PSDs
• 🧪 Well Tested: Comprehensive unit test coverage

Installation

Via Asimov (Recommended)

If you have asimov 0.7+, you can install gravitational wave pipelines including bayeswave with:

pip install asimov[gw]

This will automatically install asimov-bayeswave and other GW analysis plugins.

From PyPI (when released)

pip install asimov-bayeswave

From Source

git clone https://github.com/transientlunatic/asimov-bayeswave.git
cd asimov-bayeswave
pip install -e .

For Development

pip install -e ".[docs,test]"

Quick Start

Once installed, the BayesWave pipeline is automatically available in Asimov. To add a new bayeswave analysis you can create a blueprint YAML file like the following:

kind: analysis
pipeline: bayeswave
comment: PSD generation with BayesWave
likelihood:
  sample rate: 2048
  segment length: 8
data:
  channels:
    H1: H1:GDS-CALIB_STRAIN
    L1: L1:GDS-CALIB_STRAIN
quality:
  minimum frequency:
    H1: 20
    L1: 20

Usage

Via Asimov CLI

# Build the DAG
asimov manage build --production Prod0

# Submit the job
asimov manage submit --production Prod0

# Monitor progress
asimov manage monitor

Via Python API

from asimov_bayeswave import BayesWave

# Create pipeline instance
pipeline = BayesWave(production)

# Build and submit
pipeline.build_dag()
pipeline.submit_dag()

# Collect results after completion
assets = pipeline.collect_assets()
psds = assets["psds"]
xml_psds = assets["xml psds"]

Requirements

• Python >= 3.9
• asimov >= 0.7.0
• numpy
• BayesWave (must be installed separately)

Documentation

Full documentation is available at asimov-bayeswave.readthedocs.io.

Building Documentation Locally

cd docs
make html

The built documentation will be in docs/build/html/.

Testing

Run the test suite with:

pytest

For coverage reporting:

pytest --cov=asimov_bayeswave --cov-report=html

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please ensure:

• All tests pass
• New features include tests
• Documentation is updated
• Code follows PEP 8 style guidelines

Migration from Asimov 0.6

If you're upgrading from Asimov 0.6 which included BayesWave support natively:

1. Install this plugin: pip install asimov-bayeswave
2. The plugin will be automatically discovered by Asimov 0.7+
3. No changes to your configuration files are required

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

• Daniel Williams ([e-mail])

Acknowledgments

• The LIGO Scientific Collaboration
• The BayesWave development team
• The Asimov development team

Citation

If you use this software in your research, please cite:

@software{asimov_bayeswave,
  author = {Williams, Daniel},
  title = {asimov-bayeswave: BayesWave integration for Asimov},
  url = {https://github.com/transientlunatic/asimov-bayeswave},
  year = {2026}
}

Support

For issues, questions, or contributions, please use the GitHub issue tracker.