remove magnification_via methods

Author: Jammy2211

autoarray/inversion/inversion/mapper_valued.py

@@ -169,112 +169,4 @@ def mapped_reconstructed_image_from(
                 xp=self.mapper._xp,
             ),
             mask=self.mapper.mapper_grids.mask,
-        )
-
-    def magnification_via_mesh_from(
-        self,
-    ) -> float:
-        """
-        Returns the magnification of the reconstruction computed via the mesh, 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.
-
-        In the image-plane, this is computed by mapping the reconstruction to the image, summing all reconstructed
-        values and multiplying by the area of each image pixel. This image-plane image is not convolved with the
-        PSF, as the source plane reconstruction is a non-convolved image.
-
-        In the source-plane, this is computed by summing the reconstruction values multiplied by the area of each
-        mesh pixel, for example if the source-plane is a `Delaunay` mesh this is the area of each corresponding
-        Delaunay pixel.
-
-        This calculatiion is generally more robust that using an interpolated
-        image (see `magnification_via_interpolation_from`), because it uses the exact the source-plane reconstruction
-        values. However, certain meshes have irregular pixels, especially at the edge, which can produce large
-        areas that can artificially decrease the magnification. Including cuts on which source-plane pixels are used,
-        for example based on brightness, is recommended to ensure the magnification is robust.
-
-        Parameters
-        ----------
-        mesh_pixel_mask
-            The mask of pixels that are omitted from the reconstruction when computing the image, for example to
-            remove pixels with low signal-to-noise so they do not impact the magnification calculation.
-
-        Returns
-        -------
-        The magnification of the reconstruction computed via the mesh.
-        """
-
-        if isinstance(self.mapper, MapperDelaunay):
-            raise exc.MeshException(
-                """
-                The method `magnification_via_mesh_from` does not currently support `Delaunay` mesh objects.
-
-                To compute the magnification of a `Delaunay` mesh, use the method `magnification_via_interpolation_from`.
-
-                This method only supports a `RectangularAdaptDensity`.
-                """
-            )
-
-        mapped_reconstructed_image = self.mapped_reconstructed_image_from()
-
-        try:
-            mesh_areas = self.mapper.source_plane_mesh_grid.areas_for_magnification
-        except AttributeError:
-            mesh_areas = self.mapper.areas_for_magnification
-
-        if np.all(mesh_areas == 0.0):
-            raise exc.MeshException(
-                """
-                The magnification cannot be computed because the areas of the source-plane mesh pixels are all zero.
-
-                This probably means you have specified an invalid source-plane mesh.
-                """
-            )
-
-        return np.sum(
-            mapped_reconstructed_image * mapped_reconstructed_image.pixel_area
-        ) / np.sum(self.values_masked * mesh_areas)
-
-    def magnification_via_interpolation_from(
-        self,
-        shape_native: Tuple[int, int] = (201, 201),
-        extent: Optional[Tuple[float, float, float, float]] = None,
-    ) -> float:
-        """
-        Returns the magnification of the reconstruction computed via interpolation, 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.
-
-        In the image-plane, this is computed by mapping the reconstruction to the image, summing all reconstructed
-        values and multiplying by the area of each image pixel. This image-plane image is not convolved with the
-        PSF, as the source plane reconstruction is a non-convolved image. This image therefore does not use
-        interpolation.
-
-        In the source-plane, this is computed by interpolating the reconstruction to a regular grid of pixels, for
-        example a 2D grid of 401 x 401 pixels, and summing the reconstruction values multiplied by the area of each
-        pixel. This calculation uses interpolation to compute the source-plane image.
-
-        This calculation is generally less robust than using the mesh to compute the magnification,
-        (see `magnification_via_mesh_from`), as the interpolation may not perfectly represent the source-plane
-        reconstruction. However, it is computationally faster and can be used when the source-plane mesh has
-        irregular pixels that are not suitable for computing the magnification.
-
-        Parameters
-        ----------
-        mesh_pixel_mask
-            The mask of pixels that are omitted from the reconstruction when computing the image, for example to
-            remove pixels with low signal-to-noise so they do not impact the magnification calculation.
-
-        Returns
-        -------
-        The magnification of the reconstruction computed via interpolation.
-        """
-        mapped_reconstructed_image = self.mapped_reconstructed_image_from()
-
-        interpolated_reconstruction = self.interpolated_array_from(
-            shape_native=shape_native, extent=extent
-        )
-
-        return np.sum(
-            mapped_reconstructed_image * mapped_reconstructed_image.pixel_area
-        ) / np.sum(interpolated_reconstruction * interpolated_reconstruction.pixel_area)
+        )

autoarray/inversion/plot/inversion_plotters.py

@@ -102,7 +102,6 @@ def figures_2d_of_pixelization(
         image_pixels_per_mesh_pixel: bool = False,
         magnification_per_mesh_pixel: bool = False,
         zoom_to_brightest: bool = True,
-        interpolate_to_uniform: bool = False,
     ):
         """
         Plots the individual attributes of a specific `Mapper` of the plotter's `Inversion` object in 2D.
@@ -137,9 +136,6 @@ def figures_2d_of_pixelization(
         zoom_to_brightest
             For images not in the image-plane (e.g. the `plane_image`), whether to automatically zoom the plot to
             the brightest regions of the galaxies being plotted as opposed to the full extent of the grid.
-        interpolate_to_uniform
-            If `True`, the mapper's reconstruction is interpolated to a uniform grid before plotting, for example
-            meaning that an irregular Delaunay grid can be plotted as a uniform grid.
         """
 
         if not self.inversion.has(cls=AbstractMapper):
@@ -199,7 +195,6 @@ def figures_2d_of_pixelization(
             mapper_plotter.plot_source_from(
                 pixel_values=pixel_values,
                 zoom_to_brightest=zoom_to_brightest,
-                interpolate_to_uniform=interpolate_to_uniform,
                 auto_labels=AutoLabels(
                     title="Source Reconstruction", filename="reconstruction"
                 ),

autoarray/inversion/plot/mapper_plotters.py

@@ -47,24 +47,19 @@ def __init__(
         self.mapper = mapper
 
     def figure_2d(
-        self, interpolate_to_uniform: bool = False, solution_vector: bool = None
+        self, solution_vector: bool = None
     ):
         """
         Plots the plotter's `Mapper` object in 2D.
 
         Parameters
         ----------
-        interpolate_to_uniform
-            By default, the mesh's reconstruction is interpolated to a uniform 2D array for plotting. If the
-            reconstruction can be plotted in an alternative format (e.g. using a Delaunay mesh)
-            settings `interpolate_to_uniform=False` plots the reconstruction using this.
         solution_vector
             A vector of values which can culor the pixels of the mapper's source pixels.
         """
         self.mat_plot_2d.plot_mapper(
             mapper=self.mapper,
             visuals_2d=self.visuals_2d,
-            interpolate_to_uniform=interpolate_to_uniform,
             pixel_values=solution_vector,
             auto_labels=AutoLabels(
                 title="Pixelization Mesh (Source-Plane)", filename="mapper"
@@ -83,7 +78,7 @@ def figure_2d_image(self, image):
         )
 
     def subplot_image_and_mapper(
-        self, image: Array2D, interpolate_to_uniform: bool = False
+        self, image: Array2D,
     ):
         """
         Make a subplot of an input image and the `Mapper`'s source-plane reconstruction.
@@ -94,17 +89,13 @@ def subplot_image_and_mapper(
 
         Parameters
         ----------
-        interpolate_to_uniform
-            By default, the mesh's reconstruction is interpolated to a uniform 2D array for plotting. If the
-            reconstruction can be plotted in an alternative format (e.g. a Delaunay mesh)
-            settings `interpolate_to_uniform=False` plots the reconstruction using this.
         image
             The image which is plotted on the subplot.
         """
         self.open_subplot_figure(number_subplots=2)
 
         self.figure_2d_image(image=image)
-        self.figure_2d(interpolate_to_uniform=interpolate_to_uniform)
+        self.figure_2d()
 
         self.mat_plot_2d.output.subplot_to_figure(
             auto_filename="subplot_image_and_mapper"
@@ -115,7 +106,6 @@ def plot_source_from(
         self,
         pixel_values: np.ndarray,
         zoom_to_brightest: bool = True,
-        interpolate_to_uniform: bool = False,
         auto_labels: AutoLabels = AutoLabels(),
     ):
         """
@@ -128,9 +118,6 @@ def plot_source_from(
         zoom_to_brightest
             For images not in the image-plane (e.g. the `plane_image`), whether to automatically zoom the plot to
             the brightest regions of the galaxies being plotted as opposed to the full extent of the grid.
-        interpolate_to_uniform
-            If `True`, the mapper's reconstruction is interpolated to a uniform grid before plotting, for example
-            meaning that an irregular Delaunay grid can be plotted as a uniform grid.
         auto_labels
             The labels given to the figure.
         """
@@ -141,7 +128,6 @@ def plot_source_from(
                 auto_labels=auto_labels,
                 pixel_values=pixel_values,
                 zoom_to_brightest=zoom_to_brightest,
-                interpolate_to_uniform=interpolate_to_uniform,
             )
         except ValueError:
             logger.info(

autoarray/plot/mat_plot/two_d.py

@@ -486,7 +486,6 @@ def plot_mapper(
         mapper: MapperRectangular,
         visuals_2d: Visuals2D,
         auto_labels: AutoLabels,
-        interpolate_to_uniform: bool = False,
         pixel_values: np.ndarray = Optional[None],
         zoom_to_brightest: bool = True,
     ):
@@ -504,7 +503,6 @@ def plot_mapper(
                 mapper=mapper,
                 visuals_2d=visuals_2d,
                 auto_labels=auto_labels,
-                interpolate_to_uniform=interpolate_to_uniform,
                 pixel_values=pixel_values,
                 zoom_to_brightest=zoom_to_brightest,
             )
@@ -650,7 +648,6 @@ def _plot_delaunay_mapper(
         mapper: MapperDelaunay,
         visuals_2d: Visuals2D,
         auto_labels: AutoLabels,
-        interpolate_to_uniform: bool = False,
         pixel_values: np.ndarray = Optional[None],
         zoom_to_brightest: bool = True,
     ):

test_autoarray/inversion/inversion/test_mapper_valued.py

@@ -51,137 +51,4 @@ def test__max_pixel_list_from__filter_neighbors():
     assert pixel_list[0] == [
         0,
         8,
-    ]
-
-
-
-
-
-
-def test__magnification_via_mesh_from():
-    mask = aa.Mask2D(
-        mask=np.array(
-            [
-                [False, False, False],
-                [False, False, False],
-                [False, False, False],
-                [False, False, False],
-            ]
-        ),
-        pixel_scales=(0.5, 0.5),
-    )
-
-    magnification = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
-
-    source_plane_mesh_grid = aa.Mesh2DDelaunay(
-        values=np.array(
-            [
-                [0.0, 0.0],
-                [1.0, 0.5],
-                [0.0, 1.0],
-                [1.0, 2.0],
-                [2.0, 1.0],
-                [2.0, 2.0],
-                [2.0, 0.0],
-                [0.0, 2.0],
-                [1.0, 1.0],
-                [1.0, 1.0],
-            ]
-        ),
-    )
-
-    mapper = aa.m.MockMapper(
-        parameters=3,
-        source_plane_mesh_grid=source_plane_mesh_grid,
-        mask=mask,
-        mapping_matrix=np.ones((12, 10)),
-    )
-
-    mapper_valued = aa.MapperValued(values=magnification, mapper=mapper)
-
-    magnification = mapper_valued.magnification_via_mesh_from()
-
-    assert magnification == pytest.approx(11.7073170731, 1.0e-4)
-
-
-def test__magnification_via_mesh_from__with_pixel_mask():
-    mask = aa.Mask2D(
-        mask=np.array(
-            [
-                [False, False, False],
-                [False, False, False],
-                [False, False, False],
-                [False, False, False],
-            ]
-        ),
-        pixel_scales=(0.5, 0.5),
-    )
-
-    magnification = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
-
-    source_plane_mesh_grid = aa.Mesh2DDelaunay(
-        values=np.array(
-            [
-                [0.0, 0.0],
-                [1.0, 0.5],
-                [0.0, 1.0],
-                [1.0, 2.0],
-                [2.0, 1.0],
-                [2.0, 2.0],
-                [2.0, 0.0],
-                [0.0, 2.0],
-                [1.0, 1.0],
-                [1.0, 1.0],
-            ]
-        ),
-    )
-
-    mapper = aa.m.MockMapper(
-        parameters=3,
-        source_plane_mesh_grid=source_plane_mesh_grid,
-        mask=mask,
-        mapping_matrix=np.ones((12, 10)),
-    )
-
-    mesh_pixel_mask = np.array(
-        [True, True, True, True, True, True, True, True, False, False]
-    )
-
-    mapper_valued = aa.MapperValued(
-        values=magnification, mapper=mapper, mesh_pixel_mask=mesh_pixel_mask
-    )
-
-    magnification = mapper_valued.magnification_via_mesh_from()
-
-    assert magnification == pytest.approx(4.0, 1.0e-4)
-
-
-def test__magnification_via_interpolation_from():
-    mask = aa.Mask2D(
-        mask=np.array([[False, False], [False, False]]),
-        pixel_scales=(0.5, 0.5),
-    )
-
-    magnification = aa.Array2D(
-        values=[0.0, 1.0, 1.0, 1.0],
-        mask=mask,
-    )
-
-    mapper = aa.m.MockMapper(
-        parameters=4,
-        mask=mask,
-        interpolated_array=magnification,
-        mapping_matrix=np.ones((4, 4)),
-    )
-
-    mapper_valued = aa.MapperValued(values=np.array(magnification), mapper=mapper)
-
-    magnification = mapper_valued.magnification_via_interpolation_from()
-
-    assert magnification == pytest.approx(4.0, 1.0e-4)
-
-    magnification = mapper_valued.magnification_via_interpolation_from(
-        shape_native=(3, 3), extent=(-1.0, 1.0, -1.0, 1.0)
-    )
-
-    assert magnification == pytest.approx(4.0, 1.0e-4)
+    ]