autocti.SettingsDataset1D#

class autocti.SettingsDataset1D(grid_class: Optional[Union[Type[Grid1D], Type[Grid2D]]] = None, grid_pixelization_class: Optional[Union[Type[Grid1D], Type[Grid2D]]] = None, sub_size: int = 1, sub_size_pixelization: int = 4, fractional_accuracy: float = 0.9999, relative_accuracy: Optional[float] = None, sub_steps: Tuple[int] = (2, 4, 8, 16))[source]#

A dataset is a collection of data structures (e.g. the data, noise-map, PSF), 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_class – 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_class – 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, where the fraction accuracy is the ratio of the values computed using two grids at a higher and lower sub-grid size.

  • relative_accuracy – If the grid and / or grid_pixelization use a Grid2DIterate, this sets the relative accuracy it uses when evaluating functions, where the relative accuracy is the absolute difference of the values computed using two grids at a higher and lower sub-grid size.

  • 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: Optional[Union[Type[Grid1D], Type[Grid2D]]] = None, grid_pixelization_class: Optional[Union[Type[Grid1D], Type[Grid2D]]] = None, sub_size: int = 1, sub_size_pixelization: int = 4, fractional_accuracy: float = 0.9999, relative_accuracy: Optional[float] = None, sub_steps: Tuple[int] = (2, 4, 8, 16))#

A dataset is a collection of data structures (e.g. the data, noise-map, PSF), 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_class – 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_class – 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, where the fraction accuracy is the ratio of the values computed using two grids at a higher and lower sub-grid size.

  • relative_accuracy – If the grid and / or grid_pixelization use a Grid2DIterate, this sets the relative accuracy it uses when evaluating functions, where the relative accuracy is the absolute difference of the values computed using two grids at a higher and lower sub-grid size.

  • 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, ...])

A dataset is a collection of data structures (e.g.

grid_from(mask)

grid_pixelization_from(mask)