most tests fixed

autoarray/dataset/grids.py

@@ -146,7 +146,7 @@ def blurring(self) -> Optional[Grid2D]:
     @cached_property
     def border_relocator(self) -> BorderRelocator:
         return BorderRelocator(
-            mask=self.mask, sub_size=self.pixelization.over_sampling.sub_size
+            mask=self.mask, sub_size=self.pixelization.over_sampling_size
         )
 
 

autoarray/fixtures.py

@@ -89,6 +89,10 @@ def make_grid_2d_7x7():
     return aa.Grid2D.from_mask(mask=make_mask_2d_7x7())
 
 
+def make_grid_2d_sub_2_7x7():
+    return aa.Grid2D.from_mask(mask=make_mask_2d_7x7(), over_sampling_size=2)
+
+
 def make_grid_2d_7x7_simple():
     grid_2d_7x7 = make_grid_2d_7x7()
     grid_2d_7x7[0] = np.array([1.0, 1.0])
@@ -351,7 +355,7 @@ def make_regularization_matern_kernel():
 
 def make_rectangular_mesh_grid_3x3():
     return aa.Mesh2DRectangular.overlay_grid(
-        grid=make_over_sampler_2d_7x7().over_sampled_grid, shape_native=(3, 3)
+        grid=make_grid_2d_sub_2_7x7().grid_over_sampled, shape_native=(3, 3)
     )
 
 
@@ -410,15 +414,15 @@ def make_border_relocator_2d_7x7():
 def make_rectangular_mapper_7x7_3x3():
     mapper_grids = aa.MapperGrids(
         mask=make_mask_2d_7x7(),
-        source_plane_data_grid=make_over_sampler_2d_7x7().over_sampled_grid,
+        source_plane_data_grid=make_grid_2d_sub_2_7x7().grid_over_sampled,
         source_plane_mesh_grid=make_rectangular_mesh_grid_3x3(),
         image_plane_mesh_grid=None,
         adapt_data=aa.Array2D.ones(shape_native=(3, 3), pixel_scales=0.1),
     )
 
     return aa.MapperRectangular(
         mapper_grids=mapper_grids,
-        over_sampler=make_over_sampler_2d_7x7(),
+        over_sampler=make_grid_2d_sub_2_7x7().over_sampler,
         border_relocator=make_border_relocator_2d_7x7(),
         regularization=make_regularization_constant(),
     )
@@ -427,15 +431,15 @@ def make_rectangular_mapper_7x7_3x3():
 def make_delaunay_mapper_9_3x3():
     mapper_grids = aa.MapperGrids(
         mask=make_mask_2d_7x7(),
-        source_plane_data_grid=make_over_sampler_2d_7x7().over_sampled_grid,
+        source_plane_data_grid=make_grid_2d_sub_2_7x7().grid_over_sampled,
         source_plane_mesh_grid=make_delaunay_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.MapperDelaunay(
         mapper_grids=mapper_grids,
-        over_sampler=make_over_sampler_2d_7x7(),
+        over_sampler=make_grid_2d_sub_2_7x7().over_sampler,
         border_relocator=make_border_relocator_2d_7x7(),
         regularization=make_regularization_constant(),
     )
@@ -444,15 +448,15 @@ def make_delaunay_mapper_9_3x3():
 def make_voronoi_mapper_9_3x3():
     mapper_grids = aa.MapperGrids(
         mask=make_mask_2d_7x7(),
-        source_plane_data_grid=make_over_sampler_2d_7x7().over_sampled_grid,
+        source_plane_data_grid=make_grid_2d_sub_2_7x7().grid_over_sampled,
         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,
-        over_sampler=make_over_sampler_2d_7x7(),
+        over_sampler=make_grid_2d_sub_2_7x7().over_sampler,
         border_relocator=make_border_relocator_2d_7x7(),
         regularization=make_regularization_constant(),
     )

autoarray/inversion/linear_obj/func_list.py

@@ -96,7 +96,7 @@ def unique_mappings(self) -> UniqueMappings:
         For a `LinearObjFuncList` every data pixel's group of sub-pixels maps directly to the linear function.
         """
 
-        sub_size = np.max(self.grid.over_sampling.sub_size)
+        sub_size = np.max(self.grid.over_sampling_size)
 
         # TODO : This shape slim is prob unreliable and needs to be divided by sub_size**2
 

autoarray/inversion/pixelization/image_mesh/hilbert.py

@@ -114,7 +114,7 @@ def super_resolution_grid_from(img_2d, mask, mask_radius, pixel_scales, sub_scal
 
     over_sampler = OverSampler(mask=new_mask, sub_size=sub_scale)
 
-    new_grid = over_sampler.over_sampled_grid
+    new_grid = over_sampler.uniform_over_sampled
 
     new_img = griddata(
         points=grid, values=img_2d.ravel(), xi=new_grid, fill_value=0.0, method="linear"

autoarray/inversion/plot/inversion_plotters.py

@@ -184,7 +184,7 @@ def figures_2d_of_pixelization(
                 self.mat_plot_2d.plot_array(
                     array=array,
                     visuals_2d=self.get_visuals_2d_for_data(),
-                    grid_indexes=mapper_plotter.mapper.over_sampler.over_sampled_grid,
+                    grid_indexes=mapper_plotter.mapper.over_sampler.uniform_over_sampled,
                     auto_labels=AutoLabels(
                         title="Data Subtracted", filename="data_subtracted"
                     ),
@@ -200,7 +200,7 @@ def figures_2d_of_pixelization(
             self.mat_plot_2d.plot_array(
                 array=array,
                 visuals_2d=self.get_visuals_2d_for_data(),
-                grid_indexes=mapper_plotter.mapper.over_sampler.over_sampled_grid,
+                grid_indexes=mapper_plotter.mapper.over_sampler.uniform_over_sampled,
                 auto_labels=AutoLabels(
                     title="Reconstructed Image", filename="reconstructed_image"
                 ),

autoarray/inversion/plot/mapper_plotters.py

@@ -117,7 +117,7 @@ def subplot_image_and_mapper(
             )
 
             self.mat_plot_2d.index_scatter.scatter_grid_indexes(
-                grid=self.mapper.over_sampler.over_sampled_grid,
+                grid=self.mapper.over_sampler.uniform_over_sampled,
                 indexes=indexes,
             )
 

autoarray/operators/over_sampling/over_sample_util.py

@@ -334,7 +334,7 @@ def sub_size_radial_bins_from(
     return sub_size
 
 
-# @numba_util.jit()
+@numba_util.jit()
 def grid_2d_slim_over_sampled_via_mask_from(
     mask_2d: np.ndarray,
     pixel_scales: ty.PixelScales,

autoarray/operators/over_sampling/over_sampler.py

@@ -343,3 +343,16 @@ def slim_for_sub_slim(self) -> np.ndarray:
         return over_sample_util.slim_index_for_sub_slim_index_via_mask_2d_from(
             mask_2d=np.array(self.mask), sub_size=np.array(self.sub_size)
         ).astype("int")
+
+    @property
+    def uniform_over_sampled(self):
+        from autoarray.structures.grids.irregular_2d import Grid2DIrregular
+
+        grid = over_sample_util.grid_2d_slim_over_sampled_via_mask_from(
+            mask_2d=np.array(self.mask),
+            pixel_scales=self.mask.pixel_scales,
+            sub_size=np.array(self.sub_size).astype("int"),
+            origin=self.mask.origin,
+        )
+
+        return Grid2DIrregular(values=grid)

autoarray/plot/mat_plot/two_d.py

@@ -421,7 +421,7 @@ def plot_grid(
             ax = self.setup_subplot()
 
         if plot_over_sampled_grid:
-            grid_plot = grid.over_sampler.over_sampled_grid
+            grid_plot = grid.over_sampler.uniform_over_sampled
         else:
             grid_plot = grid
 

autoarray/structures/decorators/abstract.py

@@ -60,7 +60,7 @@ def mask(self) -> Union[Mask1D, Mask2D]:
         return self.grid.mask
 
     @property
-    def over_sampling(self) -> np.ndarray:
+    def over_sampling_size(self) -> np.ndarray:
         return self.grid.over_sampling_size
 
     def via_grid_2d(self, result):

test_autoarray/inversion/inversion/imaging/test_inversion_imaging_util.py

@@ -188,20 +188,17 @@ def test__data_vector_via_w_tilde_data_two_methods_agree():
     # TODO : Use pytest.parameterize
 
     for sub_size in range(1, 3):
-        over_sampler = aa.OverSampler(
-            mask=mask,
-            sub_size=sub_size,
-        )
-
-        grid = over_sampler.over_sampled_grid
+        grid = aa.Grid2D.from_mask(mask=mask, over_sampling_size=sub_size)
 
         mapper_grids = pixelization.mapper_grids_from(
-            mask=mask, border_relocator=None, source_plane_data_grid=grid
+            mask=mask,
+            border_relocator=None,
+            source_plane_data_grid=grid.grid_over_sampled,
         )
 
         mapper = aa.Mapper(
             mapper_grids=mapper_grids,
-            over_sampler=over_sampler,
+            over_sampler=grid.over_sampler,
             regularization=None,
         )
 
@@ -236,7 +233,7 @@ def test__data_vector_via_w_tilde_data_two_methods_agree():
             pix_sizes_for_sub_slim_index=mapper.pix_sizes_for_sub_slim_index,
             pix_weights_for_sub_slim_index=mapper.pix_weights_for_sub_slim_index,
             pix_pixels=mapper.params,
-            sub_size=np.array(over_sampler.sub_size),
+            sub_size=np.array(grid.over_sampling_size),
         )
 
         data_vector_via_w_tilde = (
@@ -319,20 +316,17 @@ def test__curvature_matrix_via_w_tilde_preload_two_methods_agree():
     pixelization = aa.mesh.Rectangular(shape=(20, 20))
 
     for sub_size in range(1, 2, 3):
-        over_sampler = aa.OverSampler(
-            mask=mask,
-            sub_size=sub_size,
-        )
-
-        grid = over_sampler.over_sampled_grid
+        grid = aa.Grid2D.from_mask(mask=mask, over_sampling_size=sub_size)
 
         mapper_grids = pixelization.mapper_grids_from(
-            mask=mask, border_relocator=None, source_plane_data_grid=grid
+            mask=mask,
+            border_relocator=None,
+            source_plane_data_grid=grid.grid_over_sampled,
         )
 
         mapper = aa.Mapper(
             mapper_grids=mapper_grids,
-            over_sampler=over_sampler,
+            over_sampler=grid.over_sampler,
             regularization=None,
         )
 
@@ -358,7 +352,7 @@ def test__curvature_matrix_via_w_tilde_preload_two_methods_agree():
             pix_sizes_for_sub_slim_index=mapper.pix_sizes_for_sub_slim_index,
             pix_weights_for_sub_slim_index=mapper.pix_weights_for_sub_slim_index,
             pix_pixels=mapper.params,
-            sub_size=np.array(over_sampler.sub_size),
+            sub_size=np.array(grid.over_sampling_size),
         )
 
         curvature_matrix_via_w_tilde = aa.util.inversion_imaging.curvature_matrix_via_w_tilde_curvature_preload_imaging_from(

test_autoarray/inversion/pixelization/mappers/test_factory.py

@@ -21,16 +21,16 @@ def test__rectangular_mapper():
 
     # Slightly manipulate input grid so sub gridding is evidence in first source pixel.
     over_sampler = aa.OverSampler(mask=mask, sub_size=2)
-    over_sampled_grid = over_sampler.over_sampled_grid
-    over_sampled_grid[0, 0] = -2.0
-    over_sampled_grid[0, 1] = 2.0
+    grid_over_sampled = over_sampler.uniform_over_sampled
+    grid_over_sampled[0, 0] = -2.0
+    grid_over_sampled[0, 1] = 2.0
 
     mesh = aa.mesh.Rectangular(shape=(3, 3))
 
     mapper_grids = mesh.mapper_grids_from(
         mask=mask,
         border_relocator=None,
-        source_plane_data_grid=over_sampled_grid,
+        source_plane_data_grid=grid_over_sampled,
         source_plane_mesh_grid=None,
     )
 
@@ -74,9 +74,9 @@ def test__delaunay_mapper():
 
     # Slightly manipulate input grid so sub gridding is evidence in first source pixel.
     over_sampler = aa.OverSampler(mask=mask, sub_size=2)
-    over_sampled_grid = over_sampler.over_sampled_grid
-    over_sampled_grid[0, 0] = -2.0
-    over_sampled_grid[0, 1] = 2.0
+    grid_over_sampled = over_sampler.uniform_over_sampled
+    grid_over_sampled[0, 0] = -2.0
+    grid_over_sampled[0, 1] = 2.0
 
     mesh = aa.mesh.Delaunay()
     image_mesh = aa.image_mesh.Overlay(shape=(3, 3))
@@ -87,7 +87,7 @@ def test__delaunay_mapper():
     mapper_grids = mesh.mapper_grids_from(
         mask=mask,
         border_relocator=None,
-        source_plane_data_grid=over_sampled_grid,
+        source_plane_data_grid=grid_over_sampled,
         source_plane_mesh_grid=image_plane_mesh_grid,
     )
 
@@ -132,9 +132,9 @@ def test__voronoi_mapper():
 
     # Slightly manipulate input grid so sub gridding is evidence in first source pixel.
     over_sampler = aa.OverSampler(mask=mask, sub_size=2)
-    over_sampled_grid = over_sampler.over_sampled_grid
-    over_sampled_grid[0, 0] = -2.0
-    over_sampled_grid[0, 1] = 2.0
+    grid_over_sampled = over_sampler.uniform_over_sampled
+    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))
@@ -145,7 +145,7 @@ def test__voronoi_mapper():
     mapper_grids = mesh.mapper_grids_from(
         mask=mask,
         border_relocator=None,
-        source_plane_data_grid=over_sampled_grid,
+        source_plane_data_grid=grid_over_sampled,
         source_plane_mesh_grid=image_plane_mesh_grid,
     )
 

test_autoarray/inversion/pixelization/test_border_relocator.py

@@ -325,17 +325,16 @@ def test__relocated_grid_from__inside_border_no_relocations():
         shape_native=(30, 30), radius=1.0, pixel_scales=(0.1, 0.1)
     )
 
-    over_sampling = aa.OverSampler(
-        mask=mask, sub_size=np.array(mask.pixels_in_mask * [2])
+    grid = aa.Grid2D.from_mask(
+        mask=mask, over_sampling_size=np.array(mask.pixels_in_mask * [2])
     )
-    grid = over_sampling.over_sampled_grid
-    grid[1, :] = [0.1, 0.1]
+    grid.grid_over_sampled[1, :] = [0.1, 0.1]
 
     border_relocator = aa.BorderRelocator(
         mask=mask, sub_size=np.array(mask.pixels_in_mask * [2])
     )
 
-    relocated_grid = border_relocator.relocated_grid_from(grid=grid)
+    relocated_grid = border_relocator.relocated_grid_from(grid=grid.grid_over_sampled)
 
     assert (relocated_grid[1] == np.array([0.1, 0.1])).all()
 
@@ -345,22 +344,21 @@ def test__relocated_grid_from__outside_border_includes_relocations():
         shape_native=(30, 30), radius=1.0, pixel_scales=(0.1, 0.1)
     )
 
-    over_sampling = aa.OverSampler(
-        mask=mask, sub_size=np.array(mask.pixels_in_mask * [2])
+    grid = aa.Grid2D.from_mask(
+        mask=mask, over_sampling_size=np.array(mask.pixels_in_mask * [2])
     )
-    grid = over_sampling.over_sampled_grid
-    grid[1, :] = [10.1, 0.1]
+    grid.grid_over_sampled[1, :] = [10.1, 0.1]
 
     border_relocator = aa.BorderRelocator(
         mask=mask, sub_size=np.array(mask.pixels_in_mask * [2])
     )
 
-    relocated_grid = border_relocator.relocated_grid_from(grid=grid)
+    relocated_grid = border_relocator.relocated_grid_from(grid=grid.grid_over_sampled)
 
     assert relocated_grid[1] == pytest.approx([0.97783243, 0.00968151], 1e-4)
 
 
-def test__relocated_grid_from__positive_origin_included_in_relocate():
+def test__relocated_grid_from__positive_origin_includeddfdsd_in_relocate():
     mask = aa.Mask2D.circular(
         shape_native=(60, 60),
         radius=1.0,
@@ -371,7 +369,7 @@ def test__relocated_grid_from__positive_origin_included_in_relocate():
     over_sampling = aa.OverSampler(
         mask=mask, sub_size=np.array(mask.pixels_in_mask * [2])
     )
-    grid = over_sampling.over_sampled_grid
+    grid = over_sampling.grid_over_sampled
     grid[1, :] = [11.1, 1.0]
 
     border_relocator = aa.BorderRelocator(
@@ -381,3 +379,23 @@ def test__relocated_grid_from__positive_origin_included_in_relocate():
     relocated_grid = border_relocator.relocated_grid_from(grid=grid)
 
     assert relocated_grid[1] == pytest.approx([1.97783243, 1.0], 1e-4)
+
+
+def test__relocated_grid_from__positive_origin_included_in_relocate():
+    mask = aa.Mask2D.circular(
+        shape_native=(60, 60),
+        radius=1.0,
+        pixel_scales=(0.1, 0.1),
+        centre=(1.0, 1.0),
+    )
+
+    grid = aa.Grid2D.from_mask(
+        mask=mask, over_sampling_size=np.array(mask.pixels_in_mask * [2])
+    )
+    grid.grid_over_sampled[1, :] = [11.1, 0.1]
+
+    border_relocator = aa.BorderRelocator(mask=mask, sub_size=grid.over_sampling_size)
+
+    relocated_grid = border_relocator.relocated_grid_from(grid=grid.grid_over_sampled)
+
+    assert relocated_grid[1] == pytest.approx([1.97783243, 1.0], 1e-4)