fix unit tests reffering to Kernel2D

Author: Jammy2211

autoarray/dataset/imaging/dataset.py

@@ -98,8 +98,6 @@ def __init__(
             over_sample_size_pixelization=over_sample_size_pixelization,
         )
 
-        self.use_normalized_psf = use_normalized_psf
-
         if self.noise_map.native is not None and check_noise_map:
             if ((self.noise_map.native <= 0.0) * np.invert(self.noise_map.mask)).any():
                 zero_entries = np.argwhere(self.noise_map.native <= 0.0)
@@ -121,7 +119,7 @@ def __init__(
 
                 state = ConvolverState(kernel=psf.kernel, mask=self.data.mask)
 
-                self.psf = Convolver(kernel=psf.kernel, state=state)
+                self.psf = Convolver(kernel=psf.kernel, state=state, normalize=use_normalized_psf)
 
         self.grids = GridsDataset(
             mask=self.data.mask,

test_autoarray/dataset/imaging/test_dataset.py

@@ -191,7 +191,7 @@ def test__apply_mask(imaging_7x7, mask_2d_7x7, psf_3x3):
         (1.0 / 3.0) * psf_3x3.slim.array, 1.0e-4
     )
 
-    assert type(masked_imaging_7x7.psf) == aa.Kernel2D
+    assert type(masked_imaging_7x7.psf) == aa.Convolver
 
 
 def test__apply_noise_scaling(imaging_7x7, mask_2d_7x7):
@@ -260,7 +260,9 @@ def test__apply_mask__noise_covariance_matrix():
 
 
 def test__different_imaging_without_mock_objects__customize_constructor_inputs():
-    psf = aa.Kernel2D.ones(shape_native=(7, 7), pixel_scales=3.0)
+
+    kernel = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=3.0)
+    psf = aa.Convolver(kernel=kernel)
 
     dataset = aa.Imaging(
         data=aa.Array2D.ones(shape_native=(19, 19), pixel_scales=3.0),
@@ -304,7 +306,8 @@ def test__psf_not_odd_x_odd_kernel__raises_error():
     with pytest.raises(exc.KernelException):
         image = aa.Array2D.ones(shape_native=(3, 3), pixel_scales=1.0)
         noise_map = aa.Array2D.ones(shape_native=(3, 3), pixel_scales=1.0)
-        psf = aa.Kernel2D.no_mask(values=[[0.0, 1.0], [1.0, 2.0]], pixel_scales=1.0)
+        kernel = aa.Array2D.no_mask(values=[[0.0, 1.0], [1.0, 2.0]], pixel_scales=1.0)
+        psf = aa.Convolver(kernel=kernel)
 
         dataset = aa.Imaging(
             data=image, noise_map=noise_map, psf=psf, disable_fft_pad=True

test_autoarray/dataset/imaging/test_simulator.py

@@ -94,10 +94,11 @@ def test__via_image_from__psf_off__background_sky_on(image_central_delta_3x3):
 def test__via_image_from__psf_on__psf_blurs_image_with_edge_trimming(
     image_central_delta_3x3,
 ):
-    psf = aa.Kernel2D.no_mask(
+    kernel = aa.Array2D.no_mask(
         values=np.array([[0.0, 1.0, 0.0], [1.0, 2.0, 1.0], [0.0, 1.0, 0.0]]),
         pixel_scales=1.0,
     )
+    psf = aa.Convolver(kernel=kernel)
 
     simulator = aa.SimulatorImaging(
         exposure_time=1.0,
@@ -118,10 +119,11 @@ def test__via_image_from__psf_on__psf_blurs_image_with_edge_trimming(
 def test__via_image_from__psf_on__disable_poisson_noise_in_data(
     image_central_delta_3x3,
 ):
-    psf = aa.Kernel2D.no_mask(
+    kernel = aa.Array2D.no_mask(
         values=np.array([[0.0, 1.0, 0.0], [1.0, 2.0, 1.0], [0.0, 1.0, 0.0]]),
         pixel_scales=1.0,
     )
+    psf = aa.Convolver(kernel=kernel)
 
     simulator = aa.SimulatorImaging(
         exposure_time=20.0,
@@ -150,10 +152,11 @@ def test__via_image_from__psf_on__disable_poisson_noise_in_data(
 def test__via_image_from__psf_on__psf_and_noise_both_on(image_central_delta_3x3):
     image = image_central_delta_3x3 + 1.0
 
-    psf = aa.Kernel2D.no_mask(
+    kernel = aa.Array2D.no_mask(
         values=np.array([[0.0, 1.0, 0.0], [1.0, 2.0, 1.0], [0.0, 1.0, 0.0]]),
         pixel_scales=1.0,
     )
+    psf = aa.Convolver(kernel=kernel)
 
     simulator = aa.SimulatorImaging(
         exposure_time=20.0,

test_autoarray/inversion/inversion/imaging/test_inversion_imaging_util.py

@@ -192,7 +192,7 @@ def test__data_vector_via_weighted_data_two_methods_agree():
     noise_map = np.random.uniform(size=mask.shape_native)
     noise_map = aa.Array2D(values=noise_map, mask=mask)
 
-    kernel = aa.Kernel2D.from_gaussian(
+    kernel = aa.Convolver.from_gaussian(
         shape_native=(7, 7), pixel_scales=mask.pixel_scales, sigma=1.0, normalize=True
     )
 
@@ -265,7 +265,7 @@ def test__curvature_matrix_via_psf_weighted_noise_two_methods_agree():
     noise_map = np.random.uniform(size=mask.shape_native)
     noise_map = aa.Array2D(values=noise_map, mask=mask)
 
-    kernel = aa.Kernel2D.from_gaussian(
+    kernel = aa.Convolver.from_gaussian(
         shape_native=(7, 7), pixel_scales=mask.pixel_scales, sigma=1.0, normalize=True
     )
 

test_autoarray/inversion/inversion/test_abstract.py

@@ -119,7 +119,8 @@ def test__curvature_matrix__via_sparse_operator__identical_to_mapping():
     image = aa.Array2D.no_mask(values=np.random.random((7, 7)), pixel_scales=1.0)
     noise_map = aa.Array2D.no_mask(values=np.random.random((7, 7)), pixel_scales=1.0)
     kernel = np.array([[0.0, 1.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 0.0]])
-    psf = aa.Kernel2D.no_mask(values=kernel, pixel_scales=1.0)
+    psf = aa.Array2D.no_mask(values=kernel, pixel_scales=1.0)
+    psf = aa.Convolver(kernel=kernel)
 
     dataset = aa.Imaging(data=image, noise_map=noise_map, psf=psf)
 
@@ -187,8 +188,9 @@ def test__curvature_matrix_via_sparse_operator__includes_source_interpolation__i
 
     image = aa.Array2D.no_mask(values=np.random.random((7, 7)), pixel_scales=1.0)
     noise_map = aa.Array2D.no_mask(values=np.random.random((7, 7)), pixel_scales=1.0)
-    kernel = np.array([[0.0, 1.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 0.0]])
-    psf = aa.Kernel2D.no_mask(values=kernel, pixel_scales=1.0)
+    kernel = aa.Array2D.no_mask([[0.0, 1.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 0.0]], pixel_scales=1.0)
+
+    psf = aa.Convolver(kernel=kernel, pixel_scales=1.0)
 
     dataset = aa.Imaging(data=image, noise_map=noise_map, psf=psf)