autoarray.inversion.pixelization.mesh.DelaunayMagnification#

class DelaunayMagnification[source]#

Bases: Delaunay

An irregular mesh of Delaunay triangle pixels, which using linear barycentric interpolation are paired with a 2D grid of (y,x) coordinates. The Delaunay corners are derived in the image-plane by overlaying a uniform grid over the image.

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

The Delaunay mesh represents pixels as an irregular 2D grid of Delaunay triangles.

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 corners of Delaunay triangles in the source-plane).
source_plane_data_grid: The observed data grid mapped to the source-plane after gravitational lensing.
source_plane_mesh_grid: The corner of each Delaunay triangle 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 the three nearest Delaunay triangle corners, using a weighted interpolation scheme. Coordinates on the source_plane_data_grid are therefore given higher weights when paired with Delaunay triangle corners they are a closer distance to.

The corners of the Delaunay pixels are derived in the image-plane by overlaying a uniform grid with the input shape over the masked image data’s grid. All coordinates in this uniform grid which are contained within the mask are kept and mapped to the source-plane via gravitational lensing, where they form the Delaunay pixel corners.

Parameters: shape – The shape of the unmasked uniform grid in the image-plane which is laid over the masked image, in order to derive the image-plane (y,x) coordinates which act as the corners of the Delaunay pixels after being mapped to the source-plane via gravitational lensing.

Methods

`image_plane_mesh_grid_from`	Computes the `mesh_grid` in the image-plane, 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 Delaunay `source_plane_mesh_grid` as a `Mesh2DDelaunay` object, which provides additional functionality for performing operations that exploit the geometry of a Delaunay mesh.
`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`

Attributes

`is_stochastic`
`uses_interpolation`

image_plane_mesh_grid_from(image_plane_data_grid, adapt_data=None, settings=<autoarray.inversion.pixelization.settings.SettingsPixelization object>)[source]#

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

For a DelaunayMagnification this grid is computed by overlaying a 2D grid with dimensions shape over the masked 2D data in the image-plane, whereby all (y,x) coordinates in this grid which are not masked are retained.

Parameters

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