autolens.FitImaging#

class autolens.FitImaging(dataset, tracer, hyper_image_sky=None, 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_image_sky=None, 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[, hyper_image_sky, …])

An lens fitter, which contains the tracer’s used to perform the fit and functions to manipulate the lens dataset’s hyper_galaxies.

refit_with_new_preloads(preloads[, …])

Attributes

blurred_image

Returns the image of all light profiles in the fit’s tracer convolved with the imaging dataset’s PSF.

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

Returns the imaging data, which may have a hyper scaling performed which rescales the background sky level in order to account for uncertainty in the background sky subtraction.

figure_of_merit

galaxy_model_image_dict

A dictionary associating galaxies with their corresponding model images

grid

image

imaging

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_image

model_images_of_planes_list

noise_map

Returns the imaging 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_image

Returns the dataset’s image with all blurred light profile images in the fit’s tracer subtracted.

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.

subtracted_images_of_planes_list

total_mappers

unmasked_blurred_image

unmasked_blurred_image_of_planes_list