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`