def fit_imaging_generator_from_aggregator(
aggregator: af.Aggregator,
settings_imaging: al.SettingsImaging = None,
settings_pixelization: al.SettingsPixelization = None,
settings_inversion: al.SettingsInversion = None,
):
"""
Returns a generator of `FitImaging` objects from an input aggregator, which generates a list of the
`FitImaging` objects for every set of results loaded in the aggregator.
This is performed by mapping the `fit_imaging_from_agg_obj` with the aggregator, which sets up each fit using
only generators ensuring that manipulating the fits of large sets of results is done in a memory efficient way.
Parameters
----------
aggregator : af.Aggregator
A PyAutoFit aggregator object containing the results of PyAutoLens model-fits."""
func = partial(
fit_imaging_from_agg_obj,
settings_imaging=settings_imaging,
settings_pixelization=settings_pixelization,
settings_inversion=settings_inversion,
)
return aggregator.map(func=func)
def fit_interferometer_generator_from_aggregator(
aggregator: af.Aggregator,
settings_interferometer: al.SettingsInterferometer = None,
settings_pixelization: al.SettingsPixelization = None,
settings_inversion: al.SettingsInversion = None,
):
"""
Returns a `FitInterferometer` object from an aggregator's `SearchOutput` class, which we call an 'agg_obj' to
describe that it acts as the aggregator object for one result in the `Aggregator`. This uses the aggregator's
generator outputs such that the function can use the `Aggregator`'s map function to to create a `FitInterferometer`
generator.
The `FitInterferometer` is created.
Parameters
----------
agg_obj : af.SearchOutput
A PyAutoFit aggregator's SearchOutput object containing the generators of the results of PyAutoLens model-fits.
"""
func = partial(
fit_interferometer_from_agg_obj,
settings_interferometer=settings_interferometer,
settings_pixelization=settings_pixelization,
settings_inversion=settings_inversion,
)
return aggregator.map(func=func)
def fit_interferometer_generator_from_aggregator(
aggregator: af.Aggregator,
settings_masked_interferometer: al.SettingsMaskedInterferometer = None,
settings_pixelization: al.SettingsPixelization = None,
settings_inversion: al.SettingsInversion = None,
):
"""
Returns a *FitInterferometer* object from an aggregator's *PhaseOutput* class, which we call an 'agg_obj' to
describe that it acts as the aggregator object for one result in the *Aggregator*. This uses the aggregator's
generator outputs such that the function can use the *Aggregator*'s map function to to create a *FitInterferometer*
generator.
The *FitInterferometer* is created following the same method as the PyAutoLens `Phase` classes.
Parameters
----------
agg_obj : af.PhaseOutput
A PyAutoFit aggregator's PhaseOutput object containing the generators of the results of PyAutoLens model-fits.
"""
func = partial(
fit_interferometer_from_agg_obj,
settings_masked_interferometer=settings_masked_interferometer,
settings_pixelization=settings_pixelization,
settings_inversion=settings_inversion,
)
return aggregator.map(func=func)
def tracer_generator_from_aggregator(aggregator: af.Aggregator):
"""
Returns a generator of `Tracer` objects from an input aggregator, which generates a list of the `Tracer` objects
for every set of results loaded in the aggregator.
This is performed by mapping the `tracer_from_agg_obj` with the aggregator, which sets up each tracer using only
generators ensuring that manipulating the planes of large sets of results is done in a memory efficient way.
Parameters
----------
aggregator : af.Aggregator
A PyAutoFit aggregator object containing the results of PyAutoLens model-fits."""
return aggregator.map(func=tracer_from_agg_obj)
def grid_search_subhalo_masses_as_array(aggregator: af.Aggregator) -> al.Array:
grid_search_result_gen = aggregator.values("grid_search_result")
grid_search_results = list(filter(None, list(grid_search_result_gen)))
if len(grid_search_results) != 1:
raise exc.AggregatorException(
"There is more than one grid search result in the aggregator - please filter the"
"aggregator.")
return grid_search_subhalo_masses_as_array_from_grid_search_result(
grid_search_result=grid_search_results[0])
def interferometer_generator_from_aggregator(
aggregator: af.Aggregator,
settings_interferometer: al.SettingsInterferometer = None):
"""
Returns a generator of `Interferometer` objects from an input aggregator, which generates a list of the
`Interferometer` objects for every set of results loaded in the aggregator.
This is performed by mapping the `interferometer_from_agg_obj` with the aggregator, which sets up each
interferometer object using only generators ensuring that manipulating the interferometer objects of large
sets of results is done in a memory efficient way.
Parameters
----------
aggregator : af.Aggregator
A PyAutoFit aggregator object containing the results of PyAutoLens model-fits."""
func = partial(interferometer_from_agg_obj,
settings_interferometer=settings_interferometer)
return aggregator.map(func=func)
def masked_imaging_generator_from_aggregator(
aggregator: af.Aggregator,
settings_masked_imaging: al.SettingsMaskedImaging = None):
"""
Returns a generator of `MaskImaging` objects from an input aggregator, which generates a list of the
`MaskImaging` objects for every set of results loaded in the aggregator.
This is performed by mapping the *masked_imaging_from_agg_obj* with the aggregator, which sets up each masked
imaging using only generators ensuring that manipulating the masked imaging of large sets of results is done in a
memory efficient way.
Parameters
----------
aggregator : af.Aggregator
A PyAutoFit aggregator object containing the results of PyAutoLens model-fits."""
func = partial(masked_imaging_from_agg_obj,
settings_masked_imaging=settings_masked_imaging)
return aggregator.map(func=func)
def grid_search_result_as_array(
aggregator: af.Aggregator,
use_log_evidences: bool = True,
use_stochastic_log_evidences: bool = False,
) -> np.ndarray:
grid_search_result_gen = aggregator.values("grid_search_result")
grid_search_results = list(filter(None, list(grid_search_result_gen)))
if len(grid_search_results) == 0:
raise exc.AggregatorException(
"There is no grid search resultin the aggregator.")
elif len(grid_search_results) > 1:
raise exc.AggregatorException(
"There is more than one grid search result in the aggregator - please filter the"
"aggregator.")
return grid_search_log_evidences_as_array_from_grid_search_result(
grid_search_result=grid_search_results[0],
use_log_evidences=use_log_evidences,
use_stochastic_log_evidences=use_stochastic_log_evidences,
)