Gravelamps
gravelamps.joint_model_selection¶
Strong Lensing Model Selection.
- gravelamps.joint_model_selection.get_likelihood(metadata: dict, defaults: dict, interferometers: list[InterferometerList], generators: list[WaveformGenerator]) Likelihood¶
Get and initialise the likelihood class based on the given information from the JSON.
- Parameters:
- metadata: dict
Event specific settings.
- defaults: dict
Defaults for all events.
- interferometers: list[InterferometerList]
Interferometric data for all images in the event.
- generators: list[WaveformGenerator]
Model specific waveform generators.
- Returns:
- Likelihood
Class that can generate the likelihood for the data to result from various parameters.
- gravelamps.joint_model_selection.get_model_result(config: dict, metadata: dict, joint_result: JointResult, model_settings: dict, signal_interferometers: list[InterferometerList]) dict¶
Gets the model specific result including the samples and the evidences.
- Parameters:
- config: dict
Global settings.
- metadata: dict
Event specific settings.
- joint_result: JointResult
Results from model agnostic sampler.
- model_settings: dict
Model specific settings.
- signal_interferometers: list[InterferometerList]
Interferometric data for all images in the event.
- Returns:
- model_resultdict
Contains keys samples which is a dictionary containing keys of each parameter and the sampled values, and evidences which contains the evaluated evidence for each sample.
- gravelamps.joint_model_selection.get_model_samples_and_interpolator(model_settings: dict, joint_result: JointResult) tuple[dict, Callable]¶
Produces the model specific samples from the model agnostic input and the specified settings as well as the posterior probability interpolator.
- Parameters:
- model_settings: dict
Model specific settings.
- joint_result: JointResult
Results from model agnostic sampler.
- Returns:
- samples: dict
Contains keys corresponding to the model parameters with values containing the posterior samples.
- interpolator: Callable
For given values of the lens model parameters returns the posterior probability.
- gravelamps.joint_model_selection.main()¶
For a given multi-image result file and specified models, constructs model specific posteriors and calculates the evidence, concluding with a model selection.
- gravelamps.joint_model_selection.make_result(metadata: dict, defaults: dict) JointResult¶
Create a JointResult object based on the given information from the JSON
- Parameters:
- metadata: dict
Event specific settings
- defaults: dict
Defaults for all events
- Returns:
- pipeline_result: JointResult
Result implementation for pipeline.