remove all Voronoi support

Author: Jammy2211

autoarray/__init__.py

@@ -42,7 +42,6 @@
 from .inversion.pixelization.mappers.factory import mapper_from as Mapper
 from .inversion.pixelization.mappers.rectangular import MapperRectangular
 from .inversion.pixelization.mappers.delaunay import MapperDelaunay
-from .inversion.pixelization.mappers.voronoi import MapperVoronoi
 from .inversion.pixelization.mappers.rectangular_uniform import MapperRectangularUniform
 from .inversion.pixelization.image_mesh.abstract import AbstractImageMesh
 from .inversion.pixelization.mesh.abstract import AbstractMesh
@@ -77,7 +76,6 @@
 from .structures.grids.irregular_2d import Grid2DIrregular
 from .structures.mesh.rectangular_2d import Mesh2DRectangular
 from .structures.mesh.rectangular_2d_uniform import Mesh2DRectangularUniform
-from .structures.mesh.voronoi_2d import Mesh2DVoronoi
 from .structures.mesh.delaunay_2d import Mesh2DDelaunay
 from .structures.arrays.kernel_2d import Kernel2D
 from .structures.vectors.uniform import VectorYX2D

autoarray/config/general.yaml

@@ -14,7 +14,5 @@ numba:
   cache: true
   nopython: true
   parallel: false
-pixelization:
-  voronoi_nn_max_interpolation_neighbors: 300
 structures:
   native_binned_only: false           # If True, data structures are only stored in their native and binned format. This is used to reduce memory usage in autocti.

autoarray/config/visualize/mat_wrap_2d.yaml

@@ -155,15 +155,6 @@ DelaunayDrawer:       # wrapper for `plt.fill()`: customize the appearance of De
     alpha: 0.7
     edgecolor: k
     linewidth: 0.0  
-VoronoiDrawer:       # wrapper for `plt.fill()`: customize the appearance of Voronoi mesh's.
-  figure:
-    alpha: 0.7
-    edgecolor: k
-    linewidth: 0.3
-  subplot:
-    alpha: 0.7
-    edgecolor: k
-    linewidth: 0.3
 ParallelOverscanPlot:
   figure:
     c: k

autoarray/fixtures.py

@@ -380,29 +380,6 @@ def make_delaunay_mesh_grid_9():
 
     return aa.Mesh2DDelaunay(values=grid_9)
 
-
-def make_voronoi_mesh_grid_9():
-    grid_9 = aa.Grid2D.no_mask(
-        values=[
-            [0.6, -0.3],
-            [0.5, -0.8],
-            [0.2, 0.1],
-            [0.0, 0.5],
-            [-0.3, -0.8],
-            [-0.6, -0.5],
-            [-0.4, -1.1],
-            [-1.2, 0.8],
-            [-1.5, 0.9],
-        ],
-        shape_native=(3, 3),
-        pixel_scales=1.0,
-    )
-
-    return aa.Mesh2DVoronoi(
-        values=grid_9,
-    )
-
-
 def make_over_sampler_2d_7x7():
     return aa.OverSampler(mask=make_mask_2d_7x7(), sub_size=2)
 
@@ -444,23 +421,6 @@ def make_delaunay_mapper_9_3x3():
         regularization=make_regularization_constant(),
     )
 
-
-def make_voronoi_mapper_9_3x3():
-    mapper_grids = aa.MapperGrids(
-        mask=make_mask_2d_7x7(),
-        source_plane_data_grid=make_grid_2d_sub_2_7x7(),
-        source_plane_mesh_grid=make_voronoi_mesh_grid_9(),
-        image_plane_mesh_grid=aa.Grid2D.uniform(shape_native=(3, 3), pixel_scales=0.1),
-        adapt_data=aa.Array2D.ones(shape_native=(3, 3), pixel_scales=0.1),
-    )
-
-    return aa.MapperVoronoi(
-        mapper_grids=mapper_grids,
-        border_relocator=make_border_relocator_2d_7x7(),
-        regularization=make_regularization_constant(),
-    )
-
-
 def make_rectangular_inversion_7x7_3x3():
     return aa.Inversion(
         dataset=make_masked_imaging_7x7(),
@@ -474,13 +434,6 @@ def make_delaunay_inversion_9_3x3():
         linear_obj_list=[make_delaunay_mapper_9_3x3()],
     )
 
-
-def make_voronoi_inversion_9_3x3():
-    return aa.Inversion(
-        dataset=make_masked_imaging_7x7(), linear_obj_list=[make_voronoi_mapper_9_3x3()]
-    )
-
-
 ### EUCLID DATA ####
 
 

autoarray/inversion/inversion/mapper_valued.py

@@ -16,7 +16,7 @@ def __init__(self, mapper, values, mesh_pixel_mask: Optional[np.ndarray] = None)
         mapper pixel) in order to perform calculations which use both the `Mapper` and these values.
 
         For example, a common use case is to interpolate the reconstruction of values on a mapper from the
-        mesh of the mapper (e.g. a Voronoi mesh) to a uniform Cartesian grid of values, because the irregular mesh
+        mesh of the mapper (e.g. a Delaunay mesh) to a uniform Cartesian grid of values, because the irregular mesh
         is difficult to plot and analyze.
 
         This class also provides functionality to compute the magnification of the reconstruction, by comparing the
@@ -26,7 +26,7 @@ def __init__(self, mapper, values, mesh_pixel_mask: Optional[np.ndarray] = None)
         Parameters
         ----------
         mapper
-            The `Mapper` object which pairs with the values, for example a `MapperVoronoi` object.
+            The `Mapper` object which pairs with the values, for example a `MapperDelaunay` object.
         values
             The values of each pixel of the mapper, which could be the `reconstruction` values of an `Inversion`,
             but alternatively could be other quantities such as the noise-map of these values.
@@ -71,7 +71,7 @@ def interpolated_array_from(
         extent: Optional[Tuple[float, float, float, float]] = None,
     ) -> Array2D:
         """
-        The values of a mapper can be on an irregular pixelization (e.g. a Delaunay triangulation, Voronoi mesh).
+        The values of a mapper can be on an irregular pixelization (e.g. a Delaunay triangulation).
 
         Analysing the reconstruction can therefore be difficult and require specific functionality tailored to using
         this irregular grid.
@@ -218,7 +218,8 @@ def magnification_via_mesh_from(
         PSF, as the source plane reconstruction is a non-convolved image.
 
         In the source-plane, this is computed by summing the reconstruction values multiplied by the area of each
-        mesh pixel, for example if the source-plane is a `Voronoi` mesh this is the area of each Voronoi pixel.
+        mesh pixel, for example if the source-plane is a `Delaunay` mesh this is the area of each corresponding
+        Delaunay pixel.
 
         This calculatiion is generally more robust that using an interpolated
         image (see `magnification_via_interpolation_from`), because it uses the exact the source-plane reconstruction
@@ -244,7 +245,7 @@ def magnification_via_mesh_from(
 
                 To compute the magnification of a `Delaunay` mesh, use the method `magnification_via_interpolation_from`.
 
-                This method only supports a `RectangularMagnification` or `Voronoi` mesh.
+                This method only supports a `RectangularMagnification`.
                 """
             )
 
@@ -257,8 +258,7 @@ def magnification_via_mesh_from(
                 """
                 The magnification cannot be computed because the areas of the source-plane mesh pixels are all zero.
 
-                This probably means you have specified an invalid source-plane mesh, for example a `Voronoi` mesh
-                where all pixels are on the edge of the source-plane and therefore have an infinite border.
+                This probably means you have specified an invalid source-plane mesh.
                 """
             )
 

autoarray/inversion/linear_obj/neighbors.py

@@ -4,8 +4,7 @@
 class Neighbors(np.ndarray):
     def __new__(cls, arr: np.ndarray, sizes: np.ndarray):
         """
-        Class packaging ndarrays describing the neighbors of every pixel in a mesh (e.g. `RectangularMagnification`,
-        `Voronoi`).
+        Class packaging ndarrays describing the neighbors of every pixel in a mesh (e.g. `RectangularMagnification`).
 
         The array `arr` contains the pixel indexes of the neighbors of every pixel. Its has shape [total_pixels,
         max_neighbors_in_single_pixel].
@@ -28,7 +27,7 @@ def __new__(cls, arr: np.ndarray, sizes: np.ndarray):
 
         - For pixel 4, the central pixel, neighbors[4,:] = [1, 3, 5, 7] and neighbors_sizes[4] = 4.
 
-        The same arrays can be generalized for other pixelizations, for example a `Voronoi` grid.
+        The same arrays can be generalized for other pixelizations, for example a `Delaunay` grid.
 
         Parameters
         ----------

autoarray/inversion/pixelization/mappers/abstract.py

@@ -420,7 +420,7 @@ def interpolated_array_from(
     ) -> Array2D:
         """
         The reconstructed values of a mapper (e.g. the `reconstruction` of an `Inversion` may be on an irregular
-        pixelization (e.g. a Delaunay triangulation, Voronoi mesh).
+        pixelization (e.g. a Delaunay triangulation).
 
         Analysing the reconstruction can therefore be difficult and require specific functionality tailored to using
         this irregular grid.
@@ -437,7 +437,7 @@ def interpolated_array_from(
         ----------
         values
             The value corresponding to the reconstructed value of every pixelization pixel (e.g. Delaunay triangle
-            vertexes, Voronoi mesh cells).
+            vertexes).
         shape_native
             The 2D shape in pixels of the interpolated reconstruction, which is always returned using square pixels.
         extent

autoarray/inversion/pixelization/mappers/factory.py

@@ -7,7 +7,6 @@
 from autoarray.structures.mesh.rectangular_2d import Mesh2DRectangular
 from autoarray.structures.mesh.rectangular_2d_uniform import Mesh2DRectangularUniform
 from autoarray.structures.mesh.delaunay_2d import Mesh2DDelaunay
-from autoarray.structures.mesh.voronoi_2d import Mesh2DVoronoi
 
 
 def mapper_from(
@@ -47,7 +46,6 @@ def mapper_from(
         MapperRectangularUniform,
     )
     from autoarray.inversion.pixelization.mappers.delaunay import MapperDelaunay
-    from autoarray.inversion.pixelization.mappers.voronoi import MapperVoronoi
 
     if isinstance(mapper_grids.source_plane_mesh_grid, Mesh2DRectangularUniform):
         return MapperRectangularUniform(
@@ -70,10 +68,3 @@ def mapper_from(
             regularization=regularization,
             xp=xp,
         )
-    elif isinstance(mapper_grids.source_plane_mesh_grid, Mesh2DVoronoi):
-        return MapperVoronoi(
-            mapper_grids=mapper_grids,
-            border_relocator=border_relocator,
-            regularization=regularization,
-            xp=xp,
-        )