autoarray.inversion.pixelization.mesh.VoronoiBrightnessImage#

class VoronoiBrightnessImage[source]#

Bases: Voronoi

An irregular mesh of Voronoi pixels, which using no interpolation are paired with a 2D grid of (y,x) coordinates. The Voronoi cell centers are derived in the image-plane by applying a KMeans clustering algorithm to the image’s weight map.

For a full description of how a mesh is paired with another grid, see the Pixelization API documentation.

The Voronoi mesh represents pixels as an irregular 2D grid of Voronoi cells.

A Pixelization using a Voronoi mesh has four grids associated with it:

image_plane_data_grid: The observed data grid in the image-plane (which is paired with the mesh in the source-plane).
image_plane_mesh_grid: The (y,x) mesh coordinates in the image-plane (which are the centres of Voronoi cells in the source-plane).
source_plane_data_grid: The observed data grid mapped to the source-plane (e.g. after gravitational lensing).
source_plane_mesh_grid: The centre of each Voronoi cell in the source-plane (the image_plane_mesh_grid maps to this after gravitational lensing).

Each (y,x) coordinate in the source_plane_data_grid is paired with all Voronoi cells it falls within, without using an interpolation scheme.

The centers of the Voronoi cell pixels are derived in the image plane, by applying a KMeans clustering algorithm to the masked image data’s weight-map. The weight_floor and weight_power allow the KMeans algorithm to adapt the image-plane coordinates to the image’s brightest or faintest values. The computed valies are mapped to the source-plane via gravitational lensing, where they form the Voronoi cell pixel centers.

Parameters

pixels – The total number of pixels in the mesh, which is therefore also the number of (y,x) coordinates computed via the KMeans clustering algorithm in image-plane.
weight_floor (float) – A parameter which reweights the data values the KMeans algorithm is applied too; as the floor increases more weight is applied to values with lower values thus allowing mesh pixels to be placed in these regions of the data.
weight_power (float) – A parameter which reweights the data values the KMeans algorithm is applied too; as the power increases more weight is applied to values with higher values thus allowing mesh pixels to be placed in these regions of the data.

Methods

`image_plane_mesh_grid_from`	Computes the mesh_grid in the data frame, by overlaying a uniform grid of coordinates over the masked 2D data (see Grid2DSparse.from_grid_and_unmasked_2d_grid_shape()).
`mapper_grids_from`	Mapper objects describe the mappings between pixels in the masked 2D data and the pixels in a mesh, in both the data and source frames.
`mesh_grid_from`	Return the Voronoi source_plane_mesh_grid as a Mesh2DVoronoi object, which provides additional functionality for performing operations that exploit the geometry of a Voronoi pixelization.
`relocated_grid_from`	Relocates all coordinates of the input source_plane_data_grid that are outside of a border (which is defined by a grid of (y,x) coordinates) to the edge of this border.
`relocated_mesh_grid_from`	Relocates all coordinates of the input source_plane_mesh_grid that are outside of a border (which is defined by a grid of (y,x) coordinates) to the edge of this border.
`weight_map_from`	Computes a weight_map from an input hyper_data, where this image represents components in the masked 2d data in the data frame.

Attributes

is_stochastic

rtype: bool

uses_interpolation

weight_map_from(hyper_data)[source]#

Computes a weight_map from an input hyper_data, where this image represents components in the masked 2d data in the data frame. This applies the weight_floor and weight_power attributes of the class, which scale the weights to make different components upweighted relative to one another.

Parameters: hyper_data (ndarray) – A image which represents one or more components in the masked 2D data in the data frame.
Return type: The weight map which is used to adapt the Voronoi pixels in the data frame to components in the data.

image_plane_mesh_grid_from(image_plane_data_grid, hyper_data, settings=<autoarray.inversion.pixelization.settings.SettingsPixelization object>)[source]#

Computes the mesh_grid in the data frame, by overlaying a uniform grid of coordinates over the masked 2D data (see Grid2DSparse.from_grid_and_unmasked_2d_grid_shape()).

The data_pixelization_grid is transformed to the source_plane_mesh_grid, and it is these (y,x) values which then act the centres of the Voronoi pixelization’s pixels.

For a VoronoiBrightnessImage this grid is computed by applying a KMeans clustering algorithm to the masked data’s values, where these values are reweighted by the hyper_data so that the algorithm can adapt to specific parts of the data.

Parameters

image_plane_mesh_grid – The sparse set of (y,x) coordinates computed from the unmasked data in the data frame. This has a transformation applied to it to create the source_plane_mesh_grid.
hyper_data (ndarray) – An image which is used to determine the image_plane_mesh_grid and therefore adapt the distribution of pixels of the Voronoi grid to the data it discretizes.
settings – Settings controlling the pixelization for example if a border is used to relocate its exterior coordinates.