all tests pass

autoarray/config/visualize/plots.yaml

@@ -9,7 +9,6 @@ dataset:                                   # Settings for plots of all datasets
   noise_map: false                         # Plot the individual noise-map of every dataset?
   signal_to_noise_map: false               # Plot the individual signal-to-noise-map of every dataset?
   over_sampling: false            # Plot the over-sampling sub-size, used to evaluate light profiles, of every dataset?
-  over_sampling_non_uniform: false  # Plot the over-sampling sub-size, used to evaluate non uniform grids, of every dataset?
   over_sampling_pixelization: false  # Plot the over-sampling sub-size, used to evaluate pixelizations, of every dataset?
 imaging:                                   # Settings for plots of imaging datasets (e.g. ImagingPlotter)
    psf: false

autoarray/dataset/over_sampling.py

@@ -36,10 +36,6 @@ def __init__(
         the image data. This grid is used specifically for pixelizations computed via the `inversion` module, which
         can benefit from using different oversampling schemes than the normal grid.
 
-        - `grid_non_uniform`: A grid of (y,x) coordinates which are mapped from the image pixel centres but have had
-        their values deflected to become non-uniform. This is used to compute over sampled light profiles of lensed
-        sources in PyAutoLens.
-
         Different calculations typically benefit from different over sampling, which this class enables
         the customization of.
 

autoarray/dataset/plot/imaging_plotters.py

@@ -62,7 +62,6 @@ def figures_2d(
         psf: bool = False,
         signal_to_noise_map: bool = False,
         over_sampling: bool = False,
-        over_sampling_non_uniform: bool = False,
         over_sampling_pixelization: bool = False,
         title_str: Optional[str] = None,
     ):
@@ -136,17 +135,6 @@ def figures_2d(
                 ),
             )
 
-        if over_sampling_non_uniform:
-            self.mat_plot_2d.plot_array(
-                array=self.dataset.grids.non_uniform.over_sampling_size,
-                visuals_2d=self.get_visuals_2d(),
-                auto_labels=AutoLabels(
-                    title=title_str or f"Over Sampling (Non Uniform)",
-                    filename="over_sampling_non_uniform",
-                    cb_unit="",
-                ),
-            )
-
         if over_sampling_pixelization:
             self.mat_plot_2d.plot_array(
                 array=self.dataset.grids.pixelization.over_sampling_size,
@@ -231,7 +219,6 @@ def subplot_dataset(self):
         self.figures_2d(signal_to_noise_map=True)
 
         self.figures_2d(over_sampling=True)
-        self.figures_2d(over_sampling_non_uniform=True)
         self.figures_2d(over_sampling_pixelization=True)
 
         self.mat_plot_2d.output.subplot_to_figure(auto_filename="subplot_dataset")

autoarray/structures/decorators/to_grid.py

@@ -22,18 +22,31 @@ def via_grid_2d(self, result) -> Union[Grid2D, List[Grid2D]]:
             The input result (e.g. of a decorated function) that is converted to a Grid2D or list of Grid2D objects.
         """
         if not isinstance(result, list):
+            try:
+                grid_over_sampled = result.grid_over_sampled
+            except AttributeError:
+                grid_over_sampled = None
+
             return Grid2D(
                 values=result,
                 mask=self.mask,
                 over_sampling_size=self.over_sampling_size,
+                grid_over_sampled=grid_over_sampled,
             )
+
+        try:
+            grid_over_sampled_list = [res.grid_over_sampled for res in result]
+        except AttributeError:
+            grid_over_sampled_list = [None] * len(result)
+
         return [
             Grid2D(
                 values=res,
                 mask=self.mask,
                 over_sampling_size=self.over_sampling_size,
+                grid_over_sampled=grid_over_sampled,
             )
-            for res in result
+            for res, grid_over_sampled in zip(result, grid_over_sampled_list)
         ]
 
     def via_grid_2d_irr(self, result) -> Union[Grid2DIrregular, List[Grid2DIrregular]]:

autoarray/structures/grids/uniform_2d.py

@@ -16,6 +16,7 @@
 from autoarray.geometry import geometry_util
 from autoarray.operators.over_sampling import over_sample_util
 
+from autoarray import exc
 from autoarray import type as ty
 
 
@@ -26,7 +27,6 @@ def __init__(
         mask: Mask2D,
         store_native: bool = False,
         over_sampling_size: Union[int, Array2D] = 4,
-        over_sampling_non_uniform: Union[int, Array2D] = 4,
         grid_over_sampled: Optional[Grid2D] = None,
         *args,
         **kwargs,
@@ -166,10 +166,6 @@ def __init__(
 
         grid_2d_util.check_grid_2d(grid_2d=values)
 
-        # self.over_sampling_non_uniform = (
-        #     over_sampling_non_uniform or OverSampling(sub_size=4)
-        # )
-
         if isinstance(over_sampling_size, int):
             over_sampling_size = np.full(
                 fill_value=over_sampling_size, shape=mask.shape_slim
@@ -1064,6 +1060,8 @@ def padded_grid_from(self, kernel_shape_native: Tuple[int, int]) -> "Grid2D":
         kernel_shape_native
             The 2D shape of the kernel which convolves signal from masked pixels to unmasked pixels.
         """
+        if kernel_shape_native[0] % 2 == 0 or kernel_shape_native[1] % 2 == 0:
+            raise exc.KernelException("Kernel2D Kernel2D must be odd")
 
         shape = self.mask.shape
 
@@ -1077,11 +1075,22 @@ def padded_grid_from(self, kernel_shape_native: Tuple[int, int]) -> "Grid2D":
             pixel_scales=self.mask.pixel_scales,
         )
 
-        return Grid2D.from_mask(
-            mask=padded_mask,
-            over_sampling_size=4,
+        pad_width = (
+            (padded_shape[0] - shape[0]) // 2,
+            (padded_shape[1] - shape[1]) // 2,
+        )
+
+        over_sampling_size = np.pad(
+            self.over_sampling_size.native,
+            pad_width,
+            mode="constant",
+            constant_values=1,
         )
 
+        over_sampling_size[over_sampling_size == 0] = 1
+
+        return Grid2D.from_mask(mask=padded_mask, over_sampling_size=over_sampling_size)
+
     @cached_property
     def is_uniform(self) -> bool:
         """

test_autoarray/dataset/plot/test_imaging_plotters.py

@@ -35,7 +35,6 @@ def test__individual_attributes_are_output(
         psf=True,
         signal_to_noise_map=True,
         over_sampling=True,
-        over_sampling_non_uniform=True,
         over_sampling_pixelization=True,
     )
 
@@ -44,7 +43,6 @@ def test__individual_attributes_are_output(
     assert path.join(plot_path, "psf.png") in plot_patch.paths
     assert path.join(plot_path, "signal_to_noise_map.png") in plot_patch.paths
     assert path.join(plot_path, "over_sampling.png") in plot_patch.paths
-    assert path.join(plot_path, "over_sampling_non_uniform.png") in plot_patch.paths
     assert path.join(plot_path, "over_sampling_pixelization.png") in plot_patch.paths
 
     plot_patch.paths = []

test_autoarray/inversion/pixelization/test_border_relocator.py

@@ -358,29 +358,6 @@ def test__relocated_grid_from__outside_border_includes_relocations():
     assert relocated_grid[1] == pytest.approx([0.97783243, 0.00968151], 1e-4)
 
 
-def test__relocated_grid_from__positive_origin_includeddfdsd_in_relocate():
-    mask = aa.Mask2D.circular(
-        shape_native=(60, 60),
-        radius=1.0,
-        pixel_scales=(0.1, 0.1),
-        centre=(1.0, 1.0),
-    )
-
-    over_sampling = aa.OverSampler(
-        mask=mask, sub_size=np.array(mask.pixels_in_mask * [2])
-    )
-    grid = over_sampling.grid_over_sampled
-    grid[1, :] = [11.1, 1.0]
-
-    border_relocator = aa.BorderRelocator(
-        mask=mask, sub_size=np.array(mask.pixels_in_mask * [2])
-    )
-
-    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),
@@ -389,10 +366,8 @@ def test__relocated_grid_from__positive_origin_included_in_relocate():
         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]
+    grid = aa.Grid2D.from_mask(mask=mask, over_sampling_size=2)
+    grid.grid_over_sampled[1, :] = [11.1, 1.0]
 
     border_relocator = aa.BorderRelocator(mask=mask, sub_size=grid.over_sampling_size)
 

test_autoarray/structures/grids/test_uniform_2d.py

@@ -665,17 +665,6 @@ def test__padded_grid_from():
     assert padded_grid.shape == (42, 2)
     assert (padded_grid == padded_grid_util).all()
 
-    grid_2d = aa.Grid2D.uniform(shape_native=(5, 5), pixel_scales=8.0)
-
-    padded_grid = grid_2d.padded_grid_from(kernel_shape_native=(2, 5))
-
-    padded_grid_util = aa.util.grid_2d.grid_2d_slim_via_mask_from(
-        mask_2d=np.full((6, 9), False), pixel_scales=(8.0, 8.0)
-    )
-
-    assert padded_grid.shape == (54, 2)
-    assert (padded_grid == padded_grid_util).all()
-
 
 def test__squared_distances_to_coordinate_from():
     mask = aa.Mask2D(