asimov-lalinference

LALInference pipeline integration for Asimov.

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

[!WARNING] LALInference has been superseded by newer sampling pipelines (bilby, RIFT) and this integration is not fully reviewed. It must not be used for collaboration parameter estimation analyses. It remains useful for cross-checks and for replicating older analyses.

Features

• 🔌 Plugin Architecture: Seamlessly integrates with Asimov via entry points
• 🚀 Scheduler-agnostic: Automated DAG generation and job submission via Asimov's HTCondor/Slurm scheduler API
• 📈 Result Collection: Automatic collection of posterior samples and results
• 🧪 Well Tested: Unit tests plus a genuine end-to-end test (real lalinference_pipe DAG generation and HTCondor execution against LALInference's own simulated noise)

Installation

LALInference itself is only distributed via conda-forge — there is no PyPI wheel for it — so installing this plugin is a two-step process:

conda install -c conda-forge lalinference
pip install asimov-lalinference

From Source

conda install -c conda-forge lalinference
git clone https://github.com/transientlunatic/asimov-lalinference.git
cd asimov-lalinference
pip install -e .

For Development

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

Quick Start

Once installed (alongside a working lalinference_pipe, from the conda-forge lalinference package), the LALInference pipeline is automatically available in Asimov via its entry-point registry — no further configuration is required beyond a normal Asimov production blueprint. See the documentation for a full example blueprint.

Requirements

• Python >= 3.10 (the conda-forge lalinference feedstock does not build for older Pythons)
• asimov >= 0.7.0
• conda-forge::lalinference — install separately with conda install -c conda-forge lalinference (there is no asimov[gw] extra in asimov core today that would pull this in automatically; each GW pipeline plugin, including this one, currently needs to be installed explicitly)

Operational notes

HTCondor pools without an IGWN-style job wrapper

lalinference_pipe generates a small lalinf_touch_output placeholder script per engine node and wires it in via custom +PreCmd/+PreArguments job classads, so that any declared output file the real executable doesn't happen to write for a given run (e.g. the SNR summary file, which isn't always produced) still exists as an empty placeholder by the time HTCondor transfers job outputs — otherwise the transfer hard-fails with something like:

Transfer output files failure ... Details: 1 total failures: first failure: reading from
file .../lalinferencenest-...-1.hdf5_snr.txt: (errno 2) No such file or directory

+-prefixed classads aren't a native HTCondor pre-exec hook — real IGWN/LIGO condor pools apparently have a USER_JOB_WRAPPER configured site-wide that interprets this convention, but a vanilla HTCondor install (e.g. the htcondor/mini test image this repo's own CI uses, or your own self-hosted pool) does not, and lalinf_touch_output silently never runs. If you hit the error above on a pool you control, configure a USER_JOB_WRAPPER that reads PreCmd/PreArguments off the job ad (available via the $_CONDOR_JOB_AD file HTCondor provides to every job) and runs it before the real executable — see .github/workflows/e2e.yml in this repo for a working example.

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.