Source code for autoarray.inversion.inversion.settings

import logging
from typing import Optional

from autoconf import conf

logger = logging.getLogger(__name__)

[docs]class SettingsInversion: def __init__( self, use_w_tilde: bool = True, use_positive_only_solver: Optional[bool] = None, positive_only_uses_p_initial: Optional[bool] = None, relocate_pix_border: Optional[bool] = None, force_edge_pixels_to_zeros: bool = True, force_edge_image_pixels_to_zeros: bool = False, image_pixels_source_zero=None, no_regularization_add_to_curvature_diag_value: float = None, use_w_tilde_numpy: bool = False, use_source_loop: bool = False, use_linear_operators: bool = False, image_mesh_min_mesh_pixels_per_pixel=None, image_mesh_min_mesh_number: int = 5, image_mesh_adapt_background_percent_threshold: float = None, image_mesh_adapt_background_percent_check: float = 0.8, tolerance: float = 1e-8, maxiter: int = 250, ): """ The settings of an Inversion, customizing how a linear set of equations are solved for. An Inversion is used to reconstruct a dataset, for example the luminous emission of a galaxy. Parameters ---------- use_w_tilde Whether to use the w-tilde formalism to perform the inversion, which speeds up the construction of the simultaneous linear equations (by bypassing the construction of a `mapping_matrix`) for many dataset use cases. use_positive_only_solver Whether to use a positive-only linear system solver, which requires that every reconstructed value is positive but is computationally much slower than the default solver (which allows for positive and negative values). relocate_pix_border If `True`, all coordinates of all pixelization source mesh grids have pixels outside their border relocated to their edge. no_regularization_add_to_curvature_diag_value If a linear func object does not have a corresponding regularization, this value is added to its diagonal entries of the curvature regularization matrix to ensure the matrix is positive-definite. use_w_tilde_numpy If True, the curvature_matrix is computed via numpy matrix multiplication (as opposed to numba functions which exploit sparsity to do the calculation normally in a more efficient way). use_source_loop Shhhh its a secret. use_linear_operators For an interferometer inversion, whether to use the linear operator solution to solve the linear system or not (this input does nothing for dataset data). image_mesh_min_mesh_pixels_per_pixel If not None, the image-mesh must place this many mesh pixels per image pixels in the N highest weighted regions of the adapt data, or an `InversionException` is raised. This can be used to force the image-mesh to cluster large numbers of source pixels to the adapt-datas brightest regions. image_mesh_min_mesh_number The value N given above in the docstring for `image_mesh_min_mesh_pixels_per_pixel`, indicating how many image pixels are checked for having a threshold number of mesh pixels. image_mesh_adapt_background_percent_threshold If not None, the image-mesh must place this percentage of mesh-pixels in the background regions of the `adapt_data`, where the background is the `image_mesh_adapt_background_percent_check` masked data pixels with the lowest values. image_mesh_adapt_background_percent_check The percentage of masked data pixels which are checked for the background criteria. tolerance For an interferometer inversion using the linear operators method, sets the tolerance of the solver (this input does nothing for dataset data and other interferometer methods). maxiter For an interferometer inversion using the linear operators method, sets the maximum number of iterations of the solver (this input does nothing for dataset data and other interferometer methods). """ self.use_w_tilde = use_w_tilde self._use_positive_only_solver = use_positive_only_solver self._positive_only_uses_p_initial = positive_only_uses_p_initial self._relocate_pix_border = relocate_pix_border self.use_linear_operators = use_linear_operators self.force_edge_pixels_to_zeros = force_edge_pixels_to_zeros self.force_edge_image_pixels_to_zeros = force_edge_image_pixels_to_zeros self.image_pixels_source_zero = image_pixels_source_zero self._no_regularization_add_to_curvature_diag_value = ( no_regularization_add_to_curvature_diag_value ) self.image_mesh_min_mesh_pixels_per_pixel = image_mesh_min_mesh_pixels_per_pixel self.image_mesh_min_mesh_number = image_mesh_min_mesh_number self.image_mesh_adapt_background_percent_threshold = ( image_mesh_adapt_background_percent_threshold ) self.image_mesh_adapt_background_percent_check = ( image_mesh_adapt_background_percent_check ) self.tolerance = tolerance self.maxiter = maxiter self.use_w_tilde_numpy = use_w_tilde_numpy self.use_source_loop = use_source_loop @property def use_positive_only_solver(self): if self._use_positive_only_solver is None: return conf.instance["general"]["inversion"]["use_positive_only_solver"] return self._use_positive_only_solver @property def positive_only_uses_p_initial(self): if self._positive_only_uses_p_initial is None: return conf.instance["general"]["inversion"]["positive_only_uses_p_initial"] return self._positive_only_uses_p_initial @property def relocate_pix_border(self): if self._relocate_pix_border is None: return conf.instance["general"]["inversion"]["relocate_pix_border"] return self._relocate_pix_border @property def no_regularization_add_to_curvature_diag_value(self): if self._no_regularization_add_to_curvature_diag_value is None: return conf.instance["general"]["inversion"][ "no_regularization_add_to_curvature_diag_value" ] return self._no_regularization_add_to_curvature_diag_value