from typing import List, Optional, Type, Union
from autoarray.dataset.abstract.settings import AbstractSettingsDataset
from autoarray.structures.grids.uniform_1d import Grid1D
from autoarray.structures.grids.uniform_2d import Grid2D
from autoarray.operators.transformer import TransformerNUFFT
[docs]class SettingsInterferometer(AbstractSettingsDataset):
[docs] def __init__(
self,
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=1,
fractional_accuracy: float = 0.9999,
sub_steps: List[int] = None,
transformer_class=TransformerNUFFT,
):
"""
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_class : ag.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_class : ag.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.
"""
super().__init__(
grid_class=grid_class,
grid_pixelization_class=grid_pixelization_class,
sub_size=sub_size,
sub_size_pixelization=sub_size_pixelization,
fractional_accuracy=fractional_accuracy,
sub_steps=sub_steps,
)
self.transformer_class = transformer_class