The `SLaM` object contains a number of methods used in the make_pipeline functions which are used to compose the model
based on the input values. It also handles pipeline tagging and path structure.
"""
slam = al.SLaM(
path_prefix=path.join("slam", dataset_name),
setup_hyper=hyper,
pipeline_source_parametric=pipeline_source_parametric,
pipeline_mass=pipeline_mass,
)
"""
__PIPELINE CREATION__
We import and make pipelines as per usual, albeit we'll now be doing this for multiple pipelines!
We then run each pipeline, passing the results of previous pipelines to subsequent pipelines.
"""
from slam.imaging.no_lens_light.pipelines import source__parametric
from slam.imaging.no_lens_light.pipelines import mass__total
source__parametric = source__parametric.make_pipeline(slam=slam,
settings=settings)
source_results = source__parametric.run(dataset=imaging, mask=mask)
mass__total = mass__total.make_pipeline(slam=slam,
settings=settings,
source_results=source_results)
mass_results = mass__total.run(dataset=imaging, mask=mask)
setup_hyper=hyper,
pipeline_source_parametric=pipeline_source_parametric,
pipeline_source_inversion=pipeline_source_inversion,
pipeline_mass=pipeline_mass,
)
"""
__PIPELINE CREATION__
We import and make pipelines as per usual, albeit we'll now be doing this for multiple pipelines!
We then run each pipeline, passing the results of previous pipelines to subsequent pipelines.
"""
from slam.imaging.no_lens_light.pipelines import source__parametric
from slam.imaging.no_lens_light.pipelines import source__inversion
from slam.imaging.no_lens_light.pipelines import mass__total
source__parametric = source__parametric.make_pipeline(slam=slam, settings=settings)
source_results = source__parametric.run(dataset=imaging, mask=mask)
source__inversion = source__inversion.make_pipeline(
slam=slam, settings=settings, source_parametric_results=source_results
)
source_results = source__inversion.run(dataset=imaging, mask=mask)
mass__total = mass__total.make_pipeline(
slam=slam, settings=settings, source_results=source_results, end_stochastic=True
)
mass_results = mass__total.run(dataset=imaging, mask=mask)
