autolens.FitInterferometer#
- class autolens.FitInterferometer(dataset, tracer, hyper_background_noise=None, use_hyper_scaling=True, settings_pixelization=<autoarray.inversion.pixelizations.settings.SettingsPixelization object>, settings_inversion=<autoarray.inversion.inversion.settings.SettingsInversion object>, preloads=<autolens.analysis.preloads.Preloads object>, profiling_dict: Optional[Dict] = None)[source]#
- __init__(dataset, tracer, hyper_background_noise=None, use_hyper_scaling=True, settings_pixelization=<autoarray.inversion.pixelizations.settings.SettingsPixelization object>, settings_inversion=<autoarray.inversion.inversion.settings.SettingsInversion object>, preloads=<autolens.analysis.preloads.Preloads object>, profiling_dict: Optional[Dict] = None)[source]#
An lens fitter, which contains the tracer’s used to perform the fit and functions to manipulate the lens dataset’s hyper_galaxies.
- Parameters
tracer (Tracer) – The tracer, which describes the ray-tracing and strong lens configuration.
Methods
__init__
(dataset, tracer[, …])An lens fitter, which contains the tracer’s used to perform the fit and functions to manipulate the lens dataset’s hyper_galaxies.
model_visibilities_of_planes
()refit_with_new_preloads
(preloads[, …])Attributes
chi_squared
Returns the chi-squared terms of the model data’s fit to an dataset, by summing the chi-squared-map.
chi_squared_map
Returns the chi-squared-map between the residual-map and noise-map, where:
data
dirty_chi_squared_map
dirty_image
dirty_model_image
dirty_noise_map
dirty_normalized_residual_map
dirty_residual_map
dirty_signal_to_noise_map
figure_of_merit
galaxy_model_image_dict
A dictionary associating galaxies with their corresponding model images
galaxy_model_visibilities_dict
A dictionary associating galaxies with their corresponding model images
grid
interferometer
inversion
A property that is only computed once per instance and then replaces itself with an ordinary attribute.
log_evidence
Returns the log evidence of the inversion’s fit to a dataset, where the log evidence includes a number of terms which quantify the complexity of an inversion’s reconstruction (see the LEq module):
log_likelihood
Returns the log likelihood of each model data point’s fit to the dataset, where:
log_likelihood_with_regularization
Returns the log likelihood of an inversion’s fit to the dataset, including a regularization term which comes from an inversion:
mask
Overwrite this method so it returns the mask of the dataset which is fitted to the input data.
model_data
Returns the model-image that is used to fit the data.
model_visibilities
noise_map
Returns the interferometer’s noise-map, which may have a hyper scaling performed which increase the noise in regions of the data that are poorly fitted in order to avoid overfitting.
noise_normalization
Returns the noise-map normalization term of the noise-map, summing the noise_map value in every pixel as:
normalized_residual_map
Returns the normalized residual-map between the masked dataset and model data, where:
potential_chi_squared_map
The signal-to-noise_map of the dataset and noise-map which are fitted.
profile_subtracted_visibilities
Returns the interferomter dataset’s visibilities with all transformed light profile images in the fit’s plane subtracted.
profile_visibilities
Returns the visibilities of every light profile in the plane, which are computed by performing a Fourier transform to the sum of light profile images.
reduced_chi_squared
residual_map
Returns the residual-map between the masked dataset and model data, where:
signal_to_noise_map
The signal-to-noise_map of the dataset and noise-map which are fitted.
total_mappers
transformer
visibilities