delete more methods

Author: Jammy2211

autoarray/inversion/inversion/mapper_valued.py

@@ -39,32 +39,6 @@ def __init__(self, mapper, values, mesh_pixel_mask: Optional[np.ndarray] = None)
         self.values = values
         self.mesh_pixel_mask = mesh_pixel_mask
 
-    @property
-    def values_masked(self):
-        """
-        Applies the `mesh_pixel_mask` to the values, setting all masked pixels to 0.0, so that mesh pixels
-        can be removed to prevent them from impacting calculations.
-
-        For example, the magnification calculation uses the values of the reconstruction to compute the magnification,
-        but this can be impacted by pixels with low signal-to-noise. The `mesh_pixel_mask` can be used to remove
-        these pixels from the calculation.
-
-        Returns
-        -------
-        The values of the mapper with the `mesh_pixel_mask` applied.
-        """
-
-        values = self.values
-
-        if self.mesh_pixel_mask is not None:
-            if self.mapper._xp.__name__.startswith("jax"):
-                values = values.at[self.mesh_pixel_mask].set(0.0)
-            else:
-                values = values.copy()
-                values[self.mesh_pixel_mask] = 0.0
-
-        return values
-
     def max_pixel_list_from(
         self, total_pixels: int = 1, filter_neighbors: bool = False
     ) -> List[List[int]]:
@@ -136,37 +110,3 @@ def max_pixel_centre(self) -> Grid2DIrregular:
         )
 
         return max_pixel_centre
-
-    def mapped_reconstructed_image_from(
-        self,
-    ) -> Array2D:
-        """
-        Returns the image of the reconstruction computed via the mapping matrix, where the image is the reconstruction
-        of the source-plane image in the image-plane without accounting for the PSF convolution.
-
-        This image is computed by mapping the reconstruction to the image, using the mapping matrix of the inversion.
-
-        The image is used to compute magnification, where the magnification is the ratio of the surface brightness of
-        image in the image-plane over the surface brightness of the source in the source-plane.
-
-        Returns
-        -------
-        The image of the reconstruction computed via the mapping matrix, with the PSF convolution not accounted for.
-        """
-
-        mapping_matrix = self.mapper.mapping_matrix
-
-        if self.mesh_pixel_mask is not None:
-            if self.mapper._xp.__name__.startswith("jax"):
-                mapping_matrix = mapping_matrix.at[:, self.mesh_pixel_mask].set(0.0)
-            else:
-                mapping_matrix[:, self.mesh_pixel_mask] = 0.0
-
-        return Array2D(
-            values=inversion_util.mapped_reconstructed_data_via_mapping_matrix_from(
-                mapping_matrix=mapping_matrix,
-                reconstruction=self.values_masked,
-                xp=self.mapper._xp,
-            ),
-            mask=self.mapper.mapper_grids.mask,
-        )

autoarray/inversion/pixelization/mappers/abstract.py

@@ -410,6 +410,7 @@ def extent_from(
             extent=self.source_plane_mesh_grid.geometry.extent
         )
 
+
 class PixSubWeights:
     def __init__(self, mappings: np.ndarray, sizes: np.ndarray, weights: np.ndarray):
         """

autoarray/inversion/pixelization/mappers/mapper_numba_util.py

@@ -1,12 +1,7 @@
 import numpy as np
 from typing import Tuple
 
-from autoconf import conf
-
 from autoarray import numba_util
-from autoarray.inversion.pixelization.mesh import mesh_numba_util
-
-from autoarray import exc
 
 
 @numba_util.jit()

autoarray/inversion/pixelization/mesh/mesh_numba_util.py

@@ -46,9 +46,7 @@ def __init__(
 
         self.mapper = mapper
 
-    def figure_2d(
-        self, solution_vector: bool = None
-    ):
+    def figure_2d(self, solution_vector: bool = None):
         """
         Plots the plotter's `Mapper` object in 2D.
 
@@ -78,7 +76,8 @@ def figure_2d_image(self, image):
         )
 
     def subplot_image_and_mapper(
-        self, image: Array2D,
+        self,
+        image: Array2D,
     ):
         """
         Make a subplot of an input image and the `Mapper`'s source-plane reconstruction.

autoarray/inversion/plot/mapper_plotters.py

@@ -11,7 +11,6 @@
 from autoarray.inversion.linear_obj.neighbors import Neighbors
 
 from autoarray import exc
-from autoarray.inversion.pixelization.mesh import mesh_numba_util
 
 
 def scipy_delaunay(points_np, query_points_np, use_voronoi_areas, areas_factor):

autoarray/structures/mesh/delaunay_2d.py

@@ -11,7 +11,6 @@
 from autoarray.layout import layout_util as layout
 from autoarray.fit import fit_util as fit
 from autoarray.inversion.pixelization.mesh import mesh_util as mesh
-from autoarray.inversion.pixelization.mesh import mesh_numba_util as mesh_numba
 from autoarray.inversion.pixelization.mappers import mapper_util as mapper
 from autoarray.inversion.pixelization.mappers import mapper_numba_util as mapper_numba
 from autoarray.inversion.regularization import regularization_util as regularization

autoarray/util/__init__.py

@@ -51,4 +51,4 @@ def test__max_pixel_list_from__filter_neighbors():
     assert pixel_list[0] == [
         0,
         8,
-    ]
+    ]

test_autoarray/inversion/inversion/test_mapper_valued.py

@@ -3,6 +3,7 @@
 
 import autoarray as aa
 
+
 def test__neighbors(grid_2d_sub_1_7x7):
 
     mesh_grid = aa.Grid2D.no_mask(