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.

• 🔌 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 (where convert_psd_ascii2xml is available — see "Operational notes" below)
• 🚀 Scheduler-agnostic: Automated DAG generation and job submission via Asimov's HTCondor/Slurm scheduler API
• 📈 Result Collection: Automatic collection of megaplot outputs and visualizations
• 🎯 PSD Suppression: Support for suppressing frequency bands in PSDs
• 🧪 Well Tested: Unit tests plus a genuine end-to-end test (real bayeswave_pipe DAG generation and HTCondor execution — BayesWave, BayesWavePost, megaplot.py — against real GW150914 H1 GWOSC strain, waiting for a real, parseable glitch_median_PSD_forLI_H1.dat, not a smoke test)

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.

pip install asimov-bayeswave

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

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

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

# Build the DAG
asimov manage build --production Prod0

# Submit the job
asimov manage submit --production Prod0

# Monitor progress
asimov manage monitor

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"]

• Python >= 3.9
• asimov >= 0.7.0
• numpy
• BayesWave (must be installed separately) — via conda-forge:

  conda install -c conda-forge bayeswave bayeswaveutils

  bayeswave ships the compiled samplers (BayesWave, BayesWavePost, ...); bayeswave_pipe (the DAG-generation script this plugin's build_dag() shells out to) and megaplot.py/megasky.py come from the separate bayeswaveutils package. Unlike the sibling asimov-lalinference plugin, no numpy<2 pin is needed — the current conda-forge bayeswaveutils build's megaplot.py has already been patched for numpy 2.0.

after_completion() tries to convert each ascii-format PSD to XML via a convert_psd_ascii2xml executable. As of this writing that tool is not shipped by any current public conda-forge package — bayeswave, bayeswaveutils, lalinference and lalapps were all checked while building this plugin's end-to-end test, and none of them provide it (it may only exist in older or IGWN-internal environments). bayeswave does ship a BayesWaveToLALPSD executable that looks like a plausible modern replacement, but its calling convention (positional run name, requires --gnuplot output enabled during the original run, reads specific paths under waveforms/) is substantially different and has not been validated here.

This is handled gracefully rather than worked around: after_completion() catches the resulting PipelineException, logs it, and continues on to store the ascii-format PSD via store_assets() regardless (see collect_assets()["psds"]). If your environment does have a working convert_psd_ascii2xml, XML-format PSD conversion and storage will work as documented above with no changes needed. If not, downstream pipelines that specifically need an XML-format PSD (rather than the ascii format) won't get one from this plugin until someone wires up BayesWaveToLALPSD (or an equivalent) as a real replacement.

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

cd docs
make html

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

Run the unit test suite with:

pytest

For coverage reporting:

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

.github/workflows/e2e.yml runs a genuine end-to-end test on real GitHub Actions infrastructure: a real bayeswave_pipe DAG (BayesWave clean run -> BayesWavePost -> megaplot.py), submitted to and run by a real (disposable, in-container) HTCondor pool, against real (trimmed, ~32s) GW150914 H1 GWOSC strain vendored into tests/test_data/frames/. It waits for and validates a genuine, parseable glitch_median_PSD_forLI_H1.dat — the same file detect_completion()/collect_assets() themselves look for — not just "the DAG was submitted", and separately checks that a production reaches a genuinely finished/uploaded state and that the missing convert_psd_ascii2xml tool (see "Operational notes" above) is handled gracefully. It's what found several of the real bugs described in CHANGELOG.md.

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

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

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

• Daniel Williams (daniel.williams@ligo.org)

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

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}
}

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