all unit tests pass

Author: Jammy2211

autoarray/dataset/imaging/dataset.py

@@ -112,14 +112,23 @@ def __init__(
                     """
                 )
 
-        self.psf = psf
-
         if psf is not None:
+
+            if use_normalized_psf:
+
+                psf.kernel._array = np.divide(
+                    psf.kernel._array, np.sum(psf.kernel._array)
+                )
+
             if psf_setup_state:
 
                 state = ConvolverState(kernel=psf.kernel, mask=self.data.mask)
 
-                self.psf = Convolver(kernel=psf.kernel, state=state, normalize=use_normalized_psf)
+                psf = Convolver(
+                    kernel=psf.kernel, state=state, normalize=use_normalized_psf
+                )
+
+        self.psf = psf
 
         self.grids = GridsDataset(
             mask=self.data.mask,
@@ -546,7 +555,7 @@ def output_to_fits(
         self.data.output_to_fits(file_path=data_path, overwrite=overwrite)
 
         if self.psf is not None and psf_path is not None:
-            self.psf.output_to_fits(file_path=psf_path, overwrite=overwrite)
+            self.psf.kernel.output_to_fits(file_path=psf_path, overwrite=overwrite)
 
         if self.noise_map is not None and noise_map_path is not None:
             self.noise_map.output_to_fits(file_path=noise_map_path, overwrite=overwrite)

autoarray/fixtures.py

@@ -119,7 +119,10 @@ def make_image_7x7():
 
 
 def make_psf_3x3():
-    psf = aa.Array2D.no_mask(values=[[0.0, 0.5, 0.0], [0.5, 1.0, 0.5], [0.0, 0.5, 0.0]], pixel_scales=(1.0, 1.0))
+    psf = aa.Array2D.no_mask(
+        values=[[0.0, 0.5, 0.0], [0.5, 1.0, 0.5], [0.0, 0.5, 0.0]],
+        pixel_scales=(1.0, 1.0),
+    )
 
     return aa.Convolver(kernel=psf)
 

autoarray/operators/convolver.py

@@ -854,15 +854,10 @@ def convolved_image_via_real_space_np_from(
                 "This may change the correctness of the PSF convolution."
             )
 
-        print(self.kernel)
-        print(image_native)
-
         convolve_native = scipy_convolve(
             image_native, self.kernel.native.array, mode="same", method="auto"
         )
 
-        print(convolve_native)
-
         convolved_array_1d = convolve_native[state.mask.slim_to_native_tuple]
 
         return Array2D(values=convolved_array_1d, mask=image.mask)

test_autoarray/dataset/imaging/test_dataset.py

@@ -125,11 +125,13 @@ def test__from_fits():
     )
 
     assert (dataset.data.native == np.ones((3, 3))).all()
-    assert dataset.psf.native == pytest.approx((1.0 / 9.0) * np.ones((3, 3)), 1.0e-4)
+    assert dataset.psf.kernel.native == pytest.approx(
+        (1.0 / 9.0) * np.ones((3, 3)), 1.0e-4
+    )
     assert (dataset.noise_map.native == 3.0 * np.ones((3, 3))).all()
 
     assert dataset.pixel_scales == (0.1, 0.1)
-    assert dataset.psf.mask.pixel_scales == (0.1, 0.1)
+    assert dataset.psf.kernel.mask.pixel_scales == (0.1, 0.1)
     assert dataset.noise_map.mask.pixel_scales == (0.1, 0.1)
 
     dataset = aa.Imaging.from_fits(
@@ -143,11 +145,13 @@ def test__from_fits():
     )
 
     assert (dataset.data.native == np.ones((3, 3))).all()
-    assert dataset.psf.native == pytest.approx((1.0 / 9.0) * np.ones((3, 3)), 1.0e-4)
+    assert dataset.psf.kernel.native == pytest.approx(
+        (1.0 / 9.0) * np.ones((3, 3)), 1.0e-4
+    )
     assert (dataset.noise_map.native == 3.0 * np.ones((3, 3))).all()
 
     assert dataset.pixel_scales == (0.1, 0.1)
-    assert dataset.psf.mask.pixel_scales == (0.1, 0.1)
+    assert dataset.psf.kernel.mask.pixel_scales == (0.1, 0.1)
     assert dataset.noise_map.mask.pixel_scales == (0.1, 0.1)
 
 
@@ -167,7 +171,7 @@ def test__output_to_fits(imaging_7x7, test_data_path):
     )
 
     assert (dataset.data.native == np.ones((7, 7))).all()
-    assert dataset.psf.native[1, 1] == pytest.approx(0.33333, 1.0e-4)
+    assert dataset.psf.kernel.native[1, 1] == pytest.approx(0.33333, 1.0e-4)
     assert (dataset.noise_map.native == 2.0 * np.ones((7, 7))).all()
     assert dataset.pixel_scales == (0.1, 0.1)
 
@@ -187,8 +191,8 @@ def test__apply_mask(imaging_7x7, mask_2d_7x7, psf_3x3):
         == 2.0 * np.ones((7, 7)) * np.invert(mask_2d_7x7)
     ).all()
 
-    assert masked_imaging_7x7.psf.slim == pytest.approx(
-        (1.0 / 3.0) * psf_3x3.slim.array, 1.0e-4
+    assert masked_imaging_7x7.psf.kernel.slim == pytest.approx(
+        (1.0 / 3.0) * psf_3x3.kernel.slim.array, 1.0e-4
     )
 
     assert type(masked_imaging_7x7.psf) == aa.Convolver
@@ -280,7 +284,7 @@ def test__different_imaging_without_mock_objects__customize_constructor_inputs()
 
     masked_dataset = dataset.apply_mask(mask=mask)
 
-    assert masked_dataset.psf.native == pytest.approx(
+    assert masked_dataset.psf.kernel.native == pytest.approx(
         (1.0 / 49.0) * np.ones((7, 7)), 1.0e-4
     )
     assert (masked_dataset.data == np.array([1.0])).all()
@@ -310,5 +314,7 @@ def test__psf_not_odd_x_odd_kernel__raises_error():
         psf = aa.Convolver(kernel=kernel)
 
         dataset = aa.Imaging(
-            data=image, noise_map=noise_map, psf=psf,
+            data=image,
+            noise_map=noise_map,
+            psf=psf,
         )

test_autoarray/inversion/inversion/test_abstract.py

@@ -188,7 +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 = 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)
+    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)