diff --git a/autogalaxy/operate/image.py b/autogalaxy/operate/image.py
index 6a3fe668b..38d4fbbc4 100644
--- a/autogalaxy/operate/image.py
+++ b/autogalaxy/operate/image.py
@@ -192,7 +192,6 @@ def visibilities_from(
         from autogalaxy.profiles.light.abstract import LightProfile
 
         if self.has(cls=LightProfile) or isinstance(self, LightProfile):
-
             image_2d = self.image_2d_from(grid=grid, xp=xp)
             return transformer.visibilities_from(image=image_2d, xp=xp)
 
diff --git a/autogalaxy/profiles/light/linear/abstract.py b/autogalaxy/profiles/light/linear/abstract.py
index 43a0946de..a8b9f97f0 100644
--- a/autogalaxy/profiles/light/linear/abstract.py
+++ b/autogalaxy/profiles/light/linear/abstract.py
@@ -270,68 +270,6 @@ def mapping_matrix(self) -> np.ndarray:
 
         return self._xp.stack(image_2d_list, axis=1)
 
-    @cached_property
-    def operated_mapping_matrix_overrideg(self) -> Optional[np.ndarray]:
-        """
-        The inversion object takes the `mapping_matrix` of each linear object and combines it with the PSF
-        operator to perform a 2D convolution and compute the `operated_mapping_matrix`.
-
-        If this property is overwritten this operation is not performed, with the `operated_mapping_matrix` output this
-        property automatically used instead.
-
-        This is used for a linear light profile because the in-built mapping matrix convolution does not account for
-        how light profile images have flux outside the masked region which is blurred into the masked region. This
-        flux is outside the region that defines the `mapping_matrix` and thus this override is required to properly
-        incorporate it.
-
-        Returns
-        -------
-        A blurred mapping matrix of dimensions (total_mask_pixels, 1) which overrides the mapping matrix calculations
-        performed in the linear equation solvers.
-        """
-
-        if isinstance(self.light_profile_list[0], LightProfileOperated):
-            return self.mapping_matrix
-
-        # number of source pixels = number of light profiles
-        n_src = len(self.light_profile_list)
-
-        # allocate slim-form arrays for mapping matrices
-        mapping_matrix = self._xp.zeros((self.grid.shape_slim, n_src))
-        blurring_mapping_matrix = self._xp.zeros((self.blurring_grid.shape_slim, n_src))
-
-        # build each column
-        for pixel, light_profile in enumerate(self.light_profile_list):
-            if self._xp.__name__.startswith("jax"):
-                # main grid mapping for this light profile
-                mapping_matrix = mapping_matrix.at[:, pixel].set(
-                    light_profile.image_2d_from(grid=self.grid, xp=self._xp).array
-                )
-
-                # blurring grid mapping for this light profile
-                blurring_mapping_matrix = blurring_mapping_matrix.at[:, pixel].set(
-                    light_profile.image_2d_from(
-                        grid=self.blurring_grid, xp=self._xp
-                    ).array
-                )
-
-            else:
-
-                mapping_matrix[:, pixel] = light_profile.image_2d_from(
-                    grid=self.grid, xp=self._xp
-                ).array
-                blurring_mapping_matrix[:, pixel] = light_profile.image_2d_from(
-                    grid=self.blurring_grid, xp=self._xp
-                ).array
-
-        return self.psf.convolved_mapping_matrix_from(
-            mapping_matrix=mapping_matrix,
-            mask=self.grid.mask,
-            blurring_mapping_matrix=blurring_mapping_matrix,
-            blurring_mask=self.blurring_grid.mask,
-            xp=self._xp,
-        )
-
     @cached_property
     def operated_mapping_matrix_override(self) -> Optional[np.ndarray]:
         """
diff --git a/autogalaxy/profiles/mass/total/power_law.py b/autogalaxy/profiles/mass/total/power_law.py
index e739a7aab..ac2cfeefa 100644
--- a/autogalaxy/profiles/mass/total/power_law.py
+++ b/autogalaxy/profiles/mass/total/power_law.py
@@ -103,12 +103,12 @@ def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
             from scipy import special
 
             complex_angle = (
-                    2.0
-                    * b
-                    / (1.0 + self.axis_ratio(xp))
-                    * (b / R) ** (slope - 1.0)
-                    * z
-                    * special.hyp2f1(1.0, 0.5 * slope, 2.0 - 0.5 * slope, -factor * z ** 2)
+                2.0
+                * b
+                / (1.0 + self.axis_ratio(xp))
+                * (b / R) ** (slope - 1.0)
+                * z
+                * special.hyp2f1(1.0, 0.5 * slope, 2.0 - 0.5 * slope, -factor * z**2)
             )
 
         deflection_y = complex_angle.imag