API Reference¶

class asimov_lalinference.lalinference.LALInference(production, category=None)[source]¶

Bases: Pipeline

The LALInference Pipeline.

Parameters:

productionasimov.Production: The production object.
categorystr, optional: The category of the job. Defaults to “analyses”.

property config_template¶

The bundled Liquid template used to render a production’s .ini file when one doesn’t already exist in the event repository.

Asimov’s generic manage build step calls production.make_config(), which looks for this attribute on the pipeline (see Production.make_config in asimov core) before falling back to a template bundled inside asimov’s own package – which no longer ships a LALInference template now that this integration lives here.

get_prior_interface()[source]¶

Get the LALInference-specific prior interface.

Returns:

LALInferencePriorInterface: The prior interface for LALInference

detect_completion()[source]¶: Check for the production of the posterior file to signal that the job has completed.

build_dag(psds=None, user=None, clobber_psd=False, dryrun=False)[source]¶

Construct a DAG file in order to submit a production to the condor scheduler using LALInferencePipe.

Parameters:

productionstr: The production name.
psdsdict, optional: The PSDs which should be used for this DAG. If no PSDs are provided the PSD files specified in the ini file will be used instead.
userstr: The user accounting tag which should be used to run the job.
dryrun: bool: If set to true the commands will not be run, but will be printed to standard output. Defaults to False.

Raises:

PipelineException: Raised if the construction of the DAG fails.

samples()[source]¶: Collect the combined samples file for PESummary.

collect_assets()[source]¶: Gather the results assets for this job, so that a downstream production (for example a PESummary post-processing production wired up via needs:) can pick them up through production._previous_assets().

collect_logs()[source]¶: Collect all of the log files which have been produced by this production and return their contents as a dictionary.

submit_dag(dryrun=False)[source]¶

Submit a DAG file to the scheduler.

Parameters:

categorystr, optional: The category of the job. Defaults to “analyses”.
productionstr: The production name.
dryrun: bool: If set to true the commands will not be run, but will be printed to standard output. Defaults to False.

Returns:

int: The cluster ID assigned to the running DAG file.
PipelineLogger: The pipeline logger message.

Raises:

PipelineException: This will be raised if the pipeline fails to submit the job.

after_completion()[source]¶

Run PESummary on the results of this job once it has completed.

Looks up the pesummary pipeline via the asimov.pipelines entry-point group (the asimov-pesummary plugin), rather than depending on a run_pesummary method that doesn’t exist anywhere in this class.

resurrect()[source]¶: Attempt to ressurrect a failed job.

classmethod read_ini(filepath)[source]¶

Read and parse a LALInference configuration file.

Parameters:

filepath: str: The path to the ini file.

class asimov_lalinference.lalinference.LALInferencePriorInterface(prior_dict=None)[source]¶

Bases: PriorInterface

Prior interface for the LALInference pipeline.

Converts asimov prior specifications into LALInference format. LALInference uses different naming conventions and expects priors as ranges (min/max values) rather than distribution objects.

Parameters:: prior_dict (Dict | PriorDict | None)

convert()[source]¶

Convert asimov priors to LALInference format.

Returns:

dict: Dictionary with LALInference-specific prior format

Return type:

Dict[str, Any]

get_amp_order()[source]¶

Get the amplitude order for LALInference.

Returns:

int: Amplitude order (default: 0)

Return type:

int

Notes

Prefers ‘amp order’ but falls back to ‘amplitude order’ for backward compatibility.