autolens.SettingsInterferometer#

class SettingsInterferometer[source]#

The lens dataset is the collection of data_type (image, noise-map), a mask, grid, convolver and other utilities that are used for modeling and fitting an image of a strong lens.

Whilst the image, noise-map, etc. are loaded in 2D, the lens dataset creates reduced 1D arrays of each for lens calculations.

Parameters

grid_classag.Grid2D: The type of grid used to create the image from the Galaxy and Plane. The options are Grid2D, and Grid2DIterate (see the Grid2D documentation for a description of these options).
grid_pixelization_classag.Grid2D: The type of grid used to create the grid that maps the Inversion source pixels to the data’s image-pixels. The options are Grid2D and Grid2DIterate (see the Grid2D documentation for a description of these options).
sub_size: If the grid and / or grid_pixelization use a Grid2D, this sets the sub-size used by the Grid2D.
fractional_accuracy: If the grid and / or grid_pixelization use a Grid2DIterate, this sets the fractional accuracy it uses when evaluating functions.
sub_steps[int]: If the grid and / or grid_pixelization use a Grid2DIterate, this sets the steps the sub-size is increased by to meet the fractional accuracy when evaluating functions.

__init__(grid_class=None, grid_pixelization_class=None, sub_size=1, sub_size_pixelization=1, fractional_accuracy=0.9999, sub_steps=None, transformer_class=<class 'autoarray.operators.transformer.TransformerNUFFT'>)[source]#

The lens dataset is the collection of data_type (image, noise-map), a mask, grid, convolver and other utilities that are used for modeling and fitting an image of a strong lens.

Whilst the image, noise-map, etc. are loaded in 2D, the lens dataset creates reduced 1D arrays of each for lens calculations.

Parameters

grid_classag.Grid2D: The type of grid used to create the image from the Galaxy and Plane. The options are Grid2D, and Grid2DIterate (see the Grid2D documentation for a description of these options).
grid_pixelization_classag.Grid2D: The type of grid used to create the grid that maps the Inversion source pixels to the data’s image-pixels. The options are Grid2D and Grid2DIterate (see the Grid2D documentation for a description of these options).
sub_size: If the grid and / or grid_pixelization use a Grid2D, this sets the sub-size used by the Grid2D.
fractional_accuracy: If the grid and / or grid_pixelization use a Grid2DIterate, this sets the fractional accuracy it uses when evaluating functions.
sub_steps[int]: If the grid and / or grid_pixelization use a Grid2DIterate, this sets the steps the sub-size is increased by to meet the fractional accuracy when evaluating functions.

Methods

__init__([grid_class, ...])

The lens dataset is the collection of data_type (image, noise-map), a mask, grid, convolver and other utilities that are used for modeling and fitting an image of a strong lens.

grid_from(mask)

rtype: Union[Grid1D, Grid2D]

grid_pixelization_from(mask)

rtype: Union[Grid1D, Grid2D]

The lens dataset is the collection of data_type (image, noise-map), a mask, grid, convolver and other utilities that are used for modeling and fitting an image of a strong lens.

Whilst the image, noise-map, etc. are loaded in 2D, the lens dataset creates reduced 1D arrays of each for lens calculations.

Parameters

grid_classag.Grid2D: The type of grid used to create the image from the Galaxy and Plane. The options are Grid2D, and Grid2DIterate (see the Grid2D documentation for a description of these options).
grid_pixelization_classag.Grid2D: The type of grid used to create the grid that maps the Inversion source pixels to the data’s image-pixels. The options are Grid2D and Grid2DIterate (see the Grid2D documentation for a description of these options).
sub_size: If the grid and / or grid_pixelization use a Grid2D, this sets the sub-size used by the Grid2D.
fractional_accuracy: If the grid and / or grid_pixelization use a Grid2DIterate, this sets the fractional accuracy it uses when evaluating functions.
sub_steps[int]: If the grid and / or grid_pixelization use a Grid2DIterate, this sets the steps the sub-size is increased by to meet the fractional accuracy when evaluating functions.

__new__(*args, **kwargs)#