Feature/image mesh jax

autoarray/inversion/inversion/settings.py

@@ -17,10 +17,6 @@ def __init__(
         no_regularization_add_to_curvature_diag_value: float = None,
         use_w_tilde_numpy: bool = False,
         use_source_loop: bool = False,
-        image_mesh_min_mesh_pixels_per_pixel=None,
-        image_mesh_min_mesh_number: int = 5,
-        image_mesh_adapt_background_percent_threshold: float = None,
-        image_mesh_adapt_background_percent_check: float = 0.8,
         tolerance: float = 1e-8,
         maxiter: int = 250,
     ):
@@ -46,19 +42,6 @@ def __init__(
             which exploit sparsity to do the calculation normally in a more efficient way).
         use_source_loop
             Shhhh its a secret.
-        image_mesh_min_mesh_pixels_per_pixel
-            If not None, the image-mesh must place this many mesh pixels per image pixels in the N highest weighted
-            regions of the adapt data, or an `InversionException` is raised. This can be used to force the image-mesh
-            to cluster large numbers of source pixels to the adapt-datas brightest regions.
-        image_mesh_min_mesh_number
-            The value N given above in the docstring for `image_mesh_min_mesh_pixels_per_pixel`, indicating how many
-            image pixels are checked for having a threshold number of mesh pixels.
-        image_mesh_adapt_background_percent_threshold
-            If not None, the image-mesh must place this percentage of mesh-pixels in the background regions of the
-            `adapt_data`, where the background is the `image_mesh_adapt_background_percent_check` masked data pixels
-            with the lowest values.
-        image_mesh_adapt_background_percent_check
-            The percentage of masked data pixels which are checked for the background criteria.
         tolerance
             For an interferometer inversion using the linear operators method, sets the tolerance of the solver
             (this input does nothing for dataset data and other interferometer methods).
@@ -73,14 +56,6 @@ def __init__(
         self._no_regularization_add_to_curvature_diag_value = (
             no_regularization_add_to_curvature_diag_value
         )
-        self.image_mesh_min_mesh_pixels_per_pixel = image_mesh_min_mesh_pixels_per_pixel
-        self.image_mesh_min_mesh_number = image_mesh_min_mesh_number
-        self.image_mesh_adapt_background_percent_threshold = (
-            image_mesh_adapt_background_percent_threshold
-        )
-        self.image_mesh_adapt_background_percent_check = (
-            image_mesh_adapt_background_percent_check
-        )
 
         self.tolerance = tolerance
         self.maxiter = maxiter

autoarray/inversion/mock/mock_image_mesh.py

@@ -12,15 +12,3 @@ def __init__(self, image_plane_mesh_grid=None):
         super().__init__()
 
         self.image_plane_mesh_grid = image_plane_mesh_grid
-
-    def image_plane_mesh_grid_from(
-        self, mask: Mask2D, adapt_data: Optional[np.ndarray], settings=None
-    ) -> Grid2DIrregular:
-        if adapt_data is not None and self.image_plane_mesh_grid is not None:
-            return adapt_data * self.image_plane_mesh_grid
-
-        return self.image_plane_mesh_grid
-
-    @property
-    def uses_adapt_images(self) -> bool:
-        return False

autoarray/inversion/mock/mock_mesh.py

@@ -37,13 +37,8 @@ def image_plane_mesh_grid_from(
         self,
         mask: Mask2D,
         adapt_data,
-        settings=None,
     ):
         if adapt_data is not None and self.image_plane_mesh_grid is not None:
             return adapt_data * self.image_plane_mesh_grid
 
         return self.image_plane_mesh_grid
-
-    @property
-    def requires_image_mesh(self):
-        return False

autoarray/inversion/mock/mock_pixelization.py

@@ -7,13 +7,10 @@ def __init__(
         self,
         mesh=None,
         regularization=None,
-        image_mesh=None,
         mapper=None,
         image_plane_mesh_grid=None,
     ):
-        super().__init__(
-            mesh=mesh, regularization=regularization, image_mesh=image_mesh
-        )
+        super().__init__(mesh=mesh, regularization=regularization)
 
         self.mapper = mapper
         self.image_plane_mesh_grid = image_plane_mesh_grid

autoarray/inversion/pixelization/image_mesh/abstract.py

@@ -18,6 +18,7 @@ def __init__(self):
         """
         An abstract image mesh, which is used by pixelizations to determine the (y,x) mesh coordinates from image
         data.
+
         """
         pass
 
@@ -29,7 +30,6 @@ def image_plane_mesh_grid_from(
         self,
         mask: Mask2D,
         adapt_data: Optional[np.ndarray] = None,
-        settings: SettingsInversion = None,
     ) -> Grid2DIrregular:
         raise NotImplementedError
 
@@ -65,131 +65,3 @@ def mesh_pixels_per_image_pixels_from(
         )
 
         return Array2D(values=mesh_pixels_per_image_pixels, mask=mask)
-
-    def check_mesh_pixels_per_image_pixels(
-        self, mask: Mask2D, mesh_grid: Grid2DIrregular, settings: SettingsInversion
-    ):
-        """
-        Checks the number of mesh pixels in every image pixel and raises an `InversionException` if there are fewer
-        mesh pixels inside a certain number of image-pixels than the input settings.
-
-        This allows a user to force a model-fit to use image-mesh's which cluster a large number of mesh pixels to
-        the brightest regions of the image data (E.g. the highst weighted regions).
-
-        The check works as follows:
-
-        1) Compute the 2D array of the number of mesh pixels in every masked data image pixel.
-        2) Find the number of mesh pixels in the N data pixels with the larger number of mesh pixels, where N is
-           given by `settings.image_mesh_min_mesh_number`. For example, if `settings.image_mesh_min_mesh_number=5` then
-           the number of mesh pixels in the 5 data pixels with the most data pixels is computed.
-        3) Compare the lowest value above to the value `settings.image_mesh_min_mesh_pixels_per_pixel`. If the value is
-           below this value, raise an `InversionException`.
-
-        Therefore, by settings `settings.image_mesh_min_mesh_pixels_per_pixel` to a value above 1 the code is forced
-        to adapt the image mesh enough to put many mesh pixels in the brightest image pixels.
-
-        Parameters
-        ----------
-        mask
-            The mask of the dataset being analysed, which the pixelization grid maps too. The number of
-            mesh pixels mapped inside each of this mask's image-pixels is returned.
-        mesh_grid
-            The image mesh-grid computed by the class which adapts to the data's mask. The number of image mesh pixels
-            that fall within each of the data's mask pixels is returned.
-        settings
-            The inversion settings, which have the criteria dictating if the image-mesh has clustered enough or if
-            an exception is raised.
-        """
-
-        if os.environ.get("PYAUTOFIT_TEST_MODE") == "1":
-            return
-
-        if settings is not None:
-            if settings.image_mesh_min_mesh_pixels_per_pixel is not None:
-                mesh_pixels_per_image_pixels = self.mesh_pixels_per_image_pixels_from(
-                    mask=mask, mesh_grid=mesh_grid
-                )
-
-                indices_of_highest_values = np.argsort(mesh_pixels_per_image_pixels)[
-                    -settings.image_mesh_min_mesh_number :
-                ]
-                lowest_mesh_pixels = np.min(
-                    mesh_pixels_per_image_pixels[indices_of_highest_values]
-                )
-
-                if lowest_mesh_pixels < settings.image_mesh_min_mesh_pixels_per_pixel:
-                    raise exc.InversionException()
-
-        return mesh_grid
-
-    def check_adapt_background_pixels(
-        self,
-        mask: Mask2D,
-        mesh_grid: Grid2DIrregular,
-        adapt_data: Optional[np.ndarray],
-        settings: SettingsInversion,
-    ):
-        """
-        Checks the number of mesh pixels in the background of the image-mesh and raises an `InversionException` if
-        there are fewer mesh pixels in the background than the input settings.
-
-        This allows a user to force a model-fit to use image-mesh's which cluster a minimum number of mesh pixels to
-        the faintest regions of the image data (E.g. the lowest weighted regions). This prevents too few image-mesh
-        pixels being allocated to the background of the data.
-
-        The check works as follows:
-
-        1) Find all pixels in the background of the `adapt_data`, which are N pixels with the lowest values, where N is
-           a percentage given by `settings.image_mesh_adapt_background_percent_check`. If N is 50%, then the half of
-            pixels in `adapt_data` with the lowest values will be checked.
-        2) Sum the total number of mesh pixels in these background pixels, thereby estimating the number of mesh pixels
-            assigned to background pixels.
-        3) Compare this value to the total number of mesh pixels multiplied
-           by `settings.image_mesh_adapt_background_percent_threshold` and raise an `InversionException` if the number
-           of mesh pixels is below this value, meaning the background did not have sufficient mesh pixels in it.
-
-        Therefore, by setting `settings.image_mesh_adapt_background_percent_threshold` the code is forced
-        to adapt the image mesh in a way that places many mesh pixels in the background regions.
-
-        Parameters
-        ----------
-        mask
-            The mask of the dataset being analysed, which the pixelization grid maps too. The number of
-            mesh pixels mapped inside each of this mask's image-pixels is returned.
-        mesh_grid
-            The image mesh-grid computed by the class which adapts to the data's mask. The number of image mesh pixels
-            that fall within each of the data's mask pixels is returned.
-        adapt_data
-            A image which represents one or more components in the masked 2D data in the image-plane.
-        settings
-            The inversion settings, which have the criteria dictating if the image-mesh has clustered enough or if
-            an exception is raised.
-        """
-
-        if os.environ.get("PYAUTOFIT_TEST_MODE") == "1":
-            return
-
-        if settings is not None:
-            if settings.image_mesh_adapt_background_percent_threshold is not None:
-                pixels = mesh_grid.shape[0]
-
-                pixels_in_background = int(
-                    mask.shape_slim * settings.image_mesh_adapt_background_percent_check
-                )
-
-                indices_of_lowest_values = np.argsort(adapt_data)[:pixels_in_background]
-                mask_background = np.zeros_like(adapt_data, dtype=bool)
-                mask_background[indices_of_lowest_values] = True
-
-                mesh_pixels_per_image_pixels = self.mesh_pixels_per_image_pixels_from(
-                    mask=mask, mesh_grid=mesh_grid
-                )
-
-                mesh_pixels_in_background = sum(
-                    mesh_pixels_per_image_pixels[mask_background]
-                )
-
-                if mesh_pixels_in_background < (
-                    pixels * settings.image_mesh_adapt_background_percent_threshold
-                ):
-                    raise exc.InversionException()