remove OverSamplingDataset

Author: Jammy2211

autolens/__init__.py

@@ -5,7 +5,6 @@
 from autoarray.dataset.interferometer.dataset import (
     Interferometer,
 )
-from autoarray.dataset.over_sampling import OverSamplingDataset
 from autoarray.dataset.grids import GridsInterface
 from autoarray.dataset.dataset_model import DatasetModel
 from autoarray.mask.mask_1d import Mask1D

autolens/lens/to_inversion.py

@@ -460,9 +460,7 @@ def mapper_galaxy_dict(self) -> Dict[aa.AbstractMapper, ag.Galaxy]:
                         regularization=pixelization_list[plane_index][
                             mapper_index
                         ].regularization,
-                        source_plane_data_grid=traced_grids_of_planes_list[
-                            plane_index
-                        ],
+                        source_plane_data_grid=traced_grids_of_planes_list[plane_index],
                         source_plane_mesh_grid=traced_mesh_grids_list_of_planes[
                             plane_index
                         ][mapper_index],

test_autolens/analysis/analysis/test_analysis_dataset.py

@@ -15,7 +15,6 @@
 def test__modify_before_fit__inversion_no_positions_likelihood__raises_exception(
     masked_imaging_7x7,
 ):
-
     lens = al.Galaxy(redshift=0.5, mass=al.mp.IsothermalSph())
 
     pixelization = al.Pixelization(

test_autolens/imaging/test_fit_imaging.py

@@ -212,7 +212,7 @@ def test__fit_figure_of_merit__sub_2(image_7x7, psf_3x3, noise_map_7x7, mask_2d_
         data=image_7x7,
         psf=psf_3x3,
         noise_map=noise_map_7x7,
-        over_sampling=al.OverSamplingDataset(lp=2),
+        over_sample_size_lp=2,
     )
 
     masked_imaging_7x7 = dataset.apply_mask(

test_autolens/imaging/test_simulate_and_fit_imaging.py

@@ -57,7 +57,7 @@ def test__perfect_fit__chi_squared_0():
         noise_map_path=path.join(file_path, "noise_map.fits"),
         psf_path=path.join(file_path, "psf.fits"),
         pixel_scales=0.2,
-        over_sampling=al.OverSamplingDataset(lp=1)
+        over_sample_size_lp=1
     )
 
     mask = al.Mask2D.circular(
@@ -154,7 +154,7 @@ def test__simulate_imaging_data_and_fit__linear_light_profiles_agree_with_standa
 
     masked_dataset = dataset.apply_mask(mask=mask)
     masked_dataset = masked_dataset.apply_over_sampling(
-        over_sampling=al.OverSamplingDataset(lp=1)
+        over_sample_size_lp=1
     )
 
     tracer = al.Tracer(galaxies=[lens_galaxy, source_galaxy])
@@ -263,7 +263,7 @@ def test__simulate_imaging_data_and_fit__linear_light_profiles_and_pixelization(
 
     masked_dataset = dataset.apply_mask(mask=mask)
     masked_dataset = masked_dataset.apply_over_sampling(
-        over_sampling=al.OverSamplingDataset(lp=1)
+        over_sample_size_lp=1
     )
 
     lens_galaxy_linear = al.Galaxy(
@@ -420,10 +420,8 @@ def test__simulate_imaging_data_and_fit__linear_light_profiles_and_pixelization_
         data=dataset.data,
         psf=dataset.psf,
         noise_map=dataset.noise_map,
-        over_sampling=al.OverSamplingDataset(
-            lp=2,
-            pixelization=2
-        )
+        over_sample_size_lp=2,
+        over_sample_size_pixelization=2
     )
 
     masked_dataset = dataset.apply_mask(mask=mask)
@@ -717,7 +715,7 @@ def test__fit_figure_of_merit__mge_mass_model(masked_imaging_7x7, masked_imaging
         noise_map_path=path.join(file_path, "noise_map.fits"),
         psf_path=path.join(file_path, "psf.fits"),
         pixel_scales=0.2,
-        over_sampling=al.OverSamplingDataset(lp=8)
+        over_sample_size_lp=8
     )
 
     mask = al.Mask2D.circular(
@@ -747,7 +745,7 @@ def test__fit_figure_of_merit__mge_mass_model(masked_imaging_7x7, masked_imaging
     assert fit.chi_squared == pytest.approx(5.706423629698664e-05, 1e-4)
 
     masked_dataset = masked_dataset.apply_over_sampling(
-        al.OverSamplingDataset(lp=8)
+        over_sample_size_lp=8
     )
 
     basis = al.lp_basis.Basis(

test_autolens/lens/test_to_inversion.py

@@ -57,9 +57,7 @@ def test__lp_linear_func_galaxy_dict_from(masked_imaging_7x7):
     assert lp_linear_func_list[1].light_profile_list[0] == lp_linear_1
     assert lp_linear_func_list[2].light_profile_list[0] == lp_linear_2
 
-    traced_grid_list = tracer.traced_grid_2d_list_from(
-        grid=masked_imaging_7x7.grids.lp
-    )
+    traced_grid_list = tracer.traced_grid_2d_list_from(grid=masked_imaging_7x7.grids.lp)
 
     assert lp_linear_func_list[0].grid == pytest.approx(
         masked_imaging_7x7.grids.lp, 1.0e-4

test_autolens/point/triangles/test_solver_jax.py

@@ -7,6 +7,7 @@
 import autofit as af
 import numpy as np
 from autolens import PointSolver, Tracer
+
 #
 # try:
 #     from autoarray.structures.triangles.coordinate_array.jax_coordinate_array import (
@@ -137,4 +138,4 @@
 #         source_plane_coordinate=(0.07, 0.07),
 #     )
 #
-#     assert len(result) == 5
+#     assert len(result) == 5