plotting bug?

Author: Jammy2211

autoarray/inversion/pixelization/mesh/delaunay.py

@@ -1,10 +1,14 @@
 import numpy as np
+from typing import Optional
 
+from autoarray.inversion.pixelization.mappers.mapper_grids import MapperGrids
+from autoarray.inversion.pixelization.border_relocator import BorderRelocator
+from autoarray.inversion.pixelization.mesh.abstract import AbstractMesh
 from autoarray.structures.mesh.delaunay_2d import Mesh2DDelaunay
-from autoarray.inversion.pixelization.mesh.triangulation import Triangulation
+from autoarray.structures.grids.uniform_2d import Grid2D
+from autoarray.structures.grids.irregular_2d import Grid2DIrregular
 
-
-class Delaunay(Triangulation):
+class Delaunay(AbstractMesh):
     def __init__(self):
         """
         An irregular mesh of Delaunay triangle pixels, which using linear barycentric interpolation are paired with

autoarray/plot/wrap/two_d/delaunay_drawer.py

@@ -2,6 +2,7 @@
 import numpy as np
 from typing import Optional
 
+from autoarray.inversion.pixelization.mappers.delaunay import MapperDelaunay
 from autoarray.plot.wrap.two_d.abstract import AbstractMatWrap2D
 from autoarray.plot.wrap.base.units import Units
 

autoarray/structures/mesh/delaunay_2d.py

@@ -236,7 +236,7 @@ def find_simplex(self, query_points):
         return find_simplex_from(query_points, self.points, self.simplices)
 
 
-class Abstract2DMeshTriangulation(Abstract2DMesh):
+class Mesh2DDelaunay(Abstract2DMesh):
     def __init__(self, values: Union[np.ndarray, List], _xp=np):
         """
         An irregular 2D grid of (y,x) coordinates which represents both a Delaunay triangulation and Voronoi mesh.

test_autoarray/config/visualize.yaml

@@ -267,12 +267,3 @@ mat_wrap_2d:
       linewidth: 51
       pivot: middle1
       units: xy1
-  VoronoiDrawer:
-    figure:
-      alpha: 0.7
-      edgecolor: k
-      linewidth: 0.3
-    subplot:
-      alpha: 0.5
-      edgecolor: r
-      linewidth: 1.0

test_autoarray/conftest.py

@@ -256,11 +256,6 @@ def make_delaunay_mapper_9_3x3():
     return fixtures.make_delaunay_mapper_9_3x3()
 
 
-@pytest.fixture(name="voronoi_mapper_9_3x3")
-def make_voronoi_mapper_9_3x3():
-    return fixtures.make_voronoi_mapper_9_3x3()
-
-
 @pytest.fixture(name="rectangular_inversion_7x7_3x3")
 def make_rectangular_inversion_7x7_3x3():
     return fixtures.make_rectangular_inversion_7x7_3x3()
@@ -271,11 +266,6 @@ def make_delaunay_inversion_9_3x3():
     return fixtures.make_delaunay_inversion_9_3x3()
 
 
-@pytest.fixture(name="voronoi_inversion_9_3x3")
-def make_voronoi_inversion_9_3x3():
-    return fixtures.make_voronoi_inversion_9_3x3()
-
-
 @pytest.fixture(name="euclid_data")
 def make_euclid_data():
     return fixtures.make_euclid_data()

test_autoarray/inversion/inversion/test_factory.py

@@ -116,7 +116,6 @@ def test__inversion_imaging__via_mapper(
 def test__inversion_imaging__via_regularizations(
     masked_imaging_7x7_no_blur,
     delaunay_mapper_9_3x3,
-    voronoi_mapper_9_3x3,
     regularization_constant,
     regularization_constant_split,
     regularization_adaptive_brightness,
@@ -154,40 +153,6 @@ def test__inversion_imaging__via_regularizations(
     )
     assert inversion.mapped_reconstructed_image == pytest.approx(np.ones(9), 1.0e-4)
 
-    pytest.importorskip(
-        "autoarray.util.nn.nn_py",
-        reason="Voronoi C library not installed, see util.nn README.md",
-    )
-
-    mapper = copy.copy(voronoi_mapper_9_3x3)
-    mapper.regularization = regularization_constant
-
-    inversion = aa.Inversion(
-        dataset=masked_imaging_7x7_no_blur_w_tilde,
-        linear_obj_list=[mapper],
-    )
-
-    assert isinstance(inversion.linear_obj_list[0], aa.MapperVoronoi)
-    assert inversion.log_det_curvature_reg_matrix_term == pytest.approx(
-        10.66505, 1.0e-4
-    )
-    assert inversion.mapped_reconstructed_image == pytest.approx(np.ones(9), 1.0e-4)
-
-    mapper = copy.copy(voronoi_mapper_9_3x3)
-    mapper.regularization = regularization_constant_split
-
-    inversion = aa.Inversion(
-        dataset=masked_imaging_7x7_no_blur_w_tilde,
-        linear_obj_list=[mapper],
-    )
-
-    assert isinstance(inversion.linear_obj_list[0], aa.MapperVoronoi)
-    assert inversion.log_det_curvature_reg_matrix_term == pytest.approx(
-        10.37955, 1.0e-4
-    )
-    assert inversion.mapped_reconstructed_image == pytest.approx(np.ones(9), 1.0e-4)
-
-
 def test__inversion_imaging__source_pixel_zeroed_indices(
     masked_imaging_7x7_no_blur,
     rectangular_mapper_7x7_3x3,

test_autoarray/inversion/inversion/test_mapper_valued.py

@@ -6,7 +6,7 @@
 
 def test__max_pixel_list_from_and_centre():
     mapper = aa.m.MockMapper(
-        source_plane_mesh_grid=aa.Mesh2DVoronoi(
+        source_plane_mesh_grid=aa.Mesh2DDelaunay(
             [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [5.0, 0.0]]
         )
     )
@@ -25,7 +25,7 @@ def test__max_pixel_list_from_and_centre():
 
 def test__max_pixel_list_from__filter_neighbors():
     mapper = aa.m.MockMapper(
-        source_plane_mesh_grid=aa.Mesh2DVoronoi(
+        source_plane_mesh_grid=aa.Mesh2DDelaunay(
             [
                 [1.0, 1.0],
                 [1.0, 2.0],
@@ -101,7 +101,7 @@ def test__magnification_via_mesh_from():
 
     magnification = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
 
-    source_plane_mesh_grid = aa.Mesh2DVoronoi(
+    source_plane_mesh_grid = aa.Mesh2DDelaunay(
         values=np.array(
             [
                 [0.0, 0.0],
@@ -147,7 +147,7 @@ def test__magnification_via_mesh_from__with_pixel_mask():
 
     magnification = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
 
-    source_plane_mesh_grid = aa.Mesh2DVoronoi(
+    source_plane_mesh_grid = aa.Mesh2DDelaunay(
         values=np.array(
             [
                 [0.0, 0.0],

test_autoarray/inversion/pixelization/mappers/test_delaunay.py

@@ -3,7 +3,7 @@
 
 import autoarray as aa
 
-from autoarray.structures.mesh.triangulation_2d import find_simplex_from
+from autoarray.structures.mesh.delaunay_2d import find_simplex_from
 from autoarray.inversion.pixelization.mappers.delaunay import (
     pix_indexes_for_sub_slim_index_delaunay_from,
 )

test_autoarray/inversion/pixelization/mappers/test_factory.py

@@ -107,56 +107,3 @@ def test__delaunay_mapper():
         1.0e-2,
     )
 
-
-def test__voronoi_mapper():
-    pytest.importorskip(
-        "autoarray.util.nn.nn_py",
-        reason="Voronoi C library not installed, see util.nn README.md",
-    )
-
-    mask = aa.Mask2D(
-        mask=[
-            [True, True, True, True, True],
-            [True, True, False, True, True],
-            [True, False, False, False, True],
-            [True, True, False, True, True],
-            [True, True, True, True, True],
-        ],
-        pixel_scales=1.0,
-    )
-
-    # Slightly manipulate input grid so sub gridding is evidence in first source pixel.
-    grid = aa.Grid2D.from_mask(mask=mask, over_sample_size=2)
-
-    grid.over_sampled[0, 0] = -2.0
-    grid.over_sampled[0, 1] = 2.0
-
-    mesh = aa.mesh.Voronoi()
-    image_mesh = aa.image_mesh.Overlay(shape=(3, 3))
-    image_plane_mesh_grid = image_mesh.image_plane_mesh_grid_from(
-        mask=mask, adapt_data=None
-    )
-
-    mapper_grids = mesh.mapper_grids_from(
-        mask=mask,
-        border_relocator=None,
-        source_plane_data_grid=grid,
-        source_plane_mesh_grid=image_plane_mesh_grid,
-    )
-
-    mapper = aa.Mapper(mapper_grids=mapper_grids, regularization=None)
-
-    assert (mapper.source_plane_mesh_grid == image_plane_mesh_grid).all()
-    assert mapper.source_plane_mesh_grid.origin == pytest.approx((0.0, 0.0), 1.0e-4)
-
-    # assert mapper.mapping_matrix == pytest.approx(
-    #     np.array(
-    #         [
-    #             [0.6875, 0.0, 0.0, 0.3125, 0.0],
-    #             [0.0, 1.0, 0.0, 0.0, 0.0],
-    #             [0.125, 0.125, 0.5, 0.125, 0.125],
-    #             [0.0, 0.0, 0.0, 0.9375, 0.0625],
-    #             [0.0, 0.0, 0.0, 0.0, 1.0],
-    #         ]
-    #     )
-    # )