remove numba relocated jit

Author: Jammy2211

autoarray/inversion/inversion/imaging_numba/inversion_imaging_numba_util.py

@@ -803,74 +803,6 @@ def mapped_reconstructed_data_via_image_to_pix_unique_from(
     return mapped_reconstructed_data
 
 
-@numba_util.jit()
-def relocated_grid_via_jit_from(grid, border_grid):
-    """
-    Relocate the coordinates of a grid to its border if they are outside the border, where the border is
-    defined as all pixels at the edge of the grid's mask (see *mask._border_1d_indexes*).
-
-    This is performed as follows:
-
-    1: Use the mean value of the grid's y and x coordinates to determine the origin of the grid.
-    2: Compute the radial distance of every grid coordinate from the origin.
-    3: For every coordinate, find its nearest pixel in the border.
-    4: Determine if it is outside the border, by comparing its radial distance from the origin to its paired
-    border pixel's radial distance.
-    5: If its radial distance is larger, use the ratio of radial distances to move the coordinate to the
-    border (if its inside the border, do nothing).
-
-    The method can be used on uniform or irregular grids, however for irregular grids the border of the
-    'image-plane' mask is used to define border pixels.
-
-    Parameters
-    ----------
-    grid
-        The grid (uniform or irregular) whose pixels are to be relocated to the border edge if outside it.
-    border_grid : Grid2D
-        The grid of border (y,x) coordinates.
-    """
-
-    grid_relocated = np.zeros(grid.shape)
-    grid_relocated[:, :] = grid[:, :]
-
-    border_origin = np.zeros(2)
-    border_origin[0] = np.mean(border_grid[:, 0])
-    border_origin[1] = np.mean(border_grid[:, 1])
-    border_grid_radii = np.sqrt(
-        np.add(
-            np.square(np.subtract(border_grid[:, 0], border_origin[0])),
-            np.square(np.subtract(border_grid[:, 1], border_origin[1])),
-        )
-    )
-    border_min_radii = np.min(border_grid_radii)
-
-    grid_radii = np.sqrt(
-        np.add(
-            np.square(np.subtract(grid[:, 0], border_origin[0])),
-            np.square(np.subtract(grid[:, 1], border_origin[1])),
-        )
-    )
-
-    for pixel_index in range(grid.shape[0]):
-        if grid_radii[pixel_index] > border_min_radii:
-            closest_pixel_index = np.argmin(
-                np.square(grid[pixel_index, 0] - border_grid[:, 0])
-                + np.square(grid[pixel_index, 1] - border_grid[:, 1])
-            )
-
-            move_factor = (
-                border_grid_radii[closest_pixel_index] / grid_radii[pixel_index]
-            )
-
-            if move_factor < 1.0:
-                grid_relocated[pixel_index, :] = (
-                    move_factor * (grid[pixel_index, :] - border_origin[:])
-                    + border_origin[:]
-                )
-
-    return grid_relocated
-
-
 class SparseLinAlgImagingNumba:
     def __init__(
         self,

autoarray/inversion/pixelization/border_relocator.py

@@ -262,6 +262,7 @@ def ellipse_params_via_border_pca_from(border_grid, xp=np, eps=1e-12):
 
     return origin, a, b, phi
 
+
 def relocated_grid_via_ellipse_border_from(grid, origin, a, b, phi, xp=np, eps=1e-12):
     """
     Rotated ellipse centered at origin with semi-axes a (major, x'), b (minor, y'),