remove mask_2d_via_shape_native_and_native_for_slim

Author: Jammy2211

autoarray/mask/mask_2d_util.py

@@ -496,50 +496,7 @@ def blurring_mask_2d_from(
         ~mask_2d + result_mask
     )
 
-    return blurring_mask  
-
-
-@numba_util.jit()
-def mask_2d_via_shape_native_and_native_for_slim(
-    shape_native: Tuple[int, int], native_for_slim: np.ndarray
-) -> np.ndarray:
-    """
-    For a slimmed set of data that was computed by mapping unmasked values from a native 2D array of shape
-    (total_y_pixels, total_x_pixels), map its slimmed indexes back to the original 2D array to create the
-    native 2D mask.
-
-    This uses an array 'native_for_slim' of shape [total_masked_pixels[ where each index gives the native 2D pixel
-    indexes of the slimmed array's unmasked pixels, for example:
-
-    - If native_for_slim[0] = [0,0], the first value of the slimmed array maps to the pixel [0,0] of the native 2D array.
-    - If native_for_slim[1] = [0,1], the second value of the slimmed array maps to the pixel [0,1] of the native 2D array.
-    - If native_for_slim[4] = [1,1], the fifth value of the slimmed array maps to the pixel [1,1] of the native 2D array.
-
-    Parameters
-    ----------
-    shape_native
-        The shape of the 2D array which the pixels are defined on.
-    native_for_slim
-        An array describing the native 2D array index that every slimmed array index maps too.
-
-    Returns
-    -------
-    ndarray
-        A 2D mask array where unmasked values are `False`.
-
-    Examples
-    --------
-    native_for_slim = np.array([[0,1], [1,0], [1,1], [1,2], [2,1]])
-
-    mask = mask_from(shape=(3,3), native_for_slim=native_for_slim)
-    """
-
-    mask = np.ones(shape_native)
-
-    for index in range(len(native_for_slim)):
-        mask[native_for_slim[index, 0], native_for_slim[index, 1]] = False
-
-    return mask
+    return blurring_mask
 
 
 @numba_util.jit()

test_autoarray/mask/test_mask_2d_util.py

@@ -668,44 +668,6 @@ def test__blurring_mask_2d_from__mask_extends_beyond_edge_so_raises_mask_excepti
         util.mask_2d.blurring_mask_2d_from(mask, kernel_shape_native=(5, 5))
 
 
-def test__mask_2d_via_shape_native_and_native_for_slim():
-    slim_to_native = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
-    shape = (2, 2)
-
-    mask = util.mask_2d.mask_2d_via_shape_native_and_native_for_slim(
-        shape_native=shape, native_for_slim=slim_to_native
-    )
-
-    assert (mask == np.array([[False, False], [False, False]])).all()
-
-    slim_to_native = np.array([[0, 0], [0, 1], [1, 0]])
-    shape = (2, 2)
-
-    mask = util.mask_2d.mask_2d_via_shape_native_and_native_for_slim(
-        shape_native=shape, native_for_slim=slim_to_native
-    )
-
-    assert (mask == np.array([[False, False], [False, True]])).all()
-
-    slim_to_native = np.array([[0, 0], [0, 1], [1, 0], [2, 0], [2, 1], [2, 3]])
-    shape = (3, 4)
-
-    mask = util.mask_2d.mask_2d_via_shape_native_and_native_for_slim(
-        shape_native=shape, native_for_slim=slim_to_native
-    )
-
-    assert (
-        mask
-        == np.array(
-            [
-                [False, False, True, True],
-                [False, True, True, True],
-                [False, False, True, False],
-            ]
-        )
-    ).all()
-
-
 def test__mask_1d_indexes_from():
     mask = np.array(
         [