def masked_interferometer_fit_for_tracer(self, tracer,
hyper_background_noise):
return fit.FitInterferometer(
masked_interferometer=self.masked_dataset,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
def masked_interferometer_fit_for_tracer(self, tracer, hyper_background_noise):
return fit.FitInterferometer(
masked_interferometer=self.masked_dataset,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
settings_pixelization=self.settings.settings_pixelization,
settings_inversion=self.settings.settings_inversion,
)
def masked_interferometer_fit_for_tracer(self,
tracer,
hyper_background_noise,
use_hyper_scalings=True):
return fit.FitInterferometer(
masked_interferometer=self.masked_dataset,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
use_hyper_scaling=use_hyper_scalings,
settings_pixelization=self.settings.settings_pixelization,
settings_inversion=self.settings.settings_inversion,
preloads=self.preloads,
)
def stochastic_log_evidences_for_instance(self, instance):
instance = self.associate_hyper_images(instance=instance)
tracer = self.tracer_for_instance(instance=instance)
if not tracer.has_pixelization:
return None
if not isinstance(
tracer.pixelizations_of_planes[-1], pix.VoronoiBrightnessImage
):
return None
hyper_background_noise = self.hyper_background_noise_for_instance(
instance=instance
)
settings_pixelization = (
self.settings.settings_pixelization.settings_with_is_stochastic_true()
)
log_evidences = []
for i in range(self.settings.settings_lens.stochastic_samples):
try:
log_evidence = fit.FitInterferometer(
masked_interferometer=self.masked_dataset,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
settings_pixelization=settings_pixelization,
settings_inversion=self.settings.settings_inversion,
).log_evidence
except (
PixelizationException,
InversionException,
GridException,
OverflowError,
) as e:
log_evidence = None
if log_evidence is not None:
log_evidences.append(log_evidence)
return log_evidences