remove native_sub_index_for_slim_sub_index_2d_from

Author: Jammy2211

autoarray/operators/over_sampling/over_sample_util.py

@@ -78,89 +78,6 @@ def total_sub_pixels_2d_from(sub_size: np.ndarray) -> int:
     return int(np.sum(sub_size**2))
 
 
-@numba_util.jit()
-def native_sub_index_for_slim_sub_index_2d_from(
-    mask_2d: np.ndarray, sub_size: np.ndarray
-) -> np.ndarray:
-    """
-    Returns an array of shape [total_unmasked_pixels*sub_size] that maps every unmasked sub-pixel to its
-    corresponding native 2D pixel using its (y,x) pixel indexes.
-
-    For example, for the following ``Mask2D`` for ``sub_size=1``:
-
-    ::
-        [[True,  True,  True, True]
-         [True, False, False, True],
-         [True, False,  True, True],
-         [True,  True,  True, True]]
-
-    This has three unmasked (``False`` values) which have the ``slim`` indexes:
-
-    ::
-        [0, 1, 2]
-
-    The array ``native_index_for_slim_index_2d`` is therefore:
-
-    ::
-        [[1,1], [1,2], [2,1]]
-
-    For a ``Mask2D`` with ``sub_size=2`` each unmasked ``False`` entry is split into a sub-pixel of size 2x2 and
-    there are therefore 12 ``slim`` indexes:
-
-    ::
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
-
-    The array ``native_index_for_slim_index_2d`` is therefore:
-
-    ::
-        [[2,2], [2,3], [2,4], [2,5], [3,2], [3,3], [3,4], [3,5], [4,2], [4,3], [5,2], [5,3]]
-
-    Parameters
-    ----------
-    mask_2d
-        A 2D array of bools, where `False` values are unmasked.
-    sub_size
-        The size of the sub-grid in each mask pixel.
-
-    Returns
-    -------
-    ndarray
-        An array that maps pixels from a slimmed array of shape [total_unmasked_pixels*sub_size] to its native array
-        of shape [total_pixels*sub_size, total_pixels*sub_size].
-
-    Examples
-    --------
-    mask_2d = np.array([[True, True, True],
-                     [True, False, True]
-                     [True, True, True]])
-
-    sub_native_index_for_sub_slim_index_2d = sub_native_index_for_sub_slim_index_via_mask_2d_from(mask_2d=mask_2d, sub_size=1)
-    """
-
-    total_sub_pixels = total_sub_pixels_2d_from(sub_size=sub_size)
-    sub_native_index_for_sub_slim_index_2d = np.zeros(shape=(total_sub_pixels, 2))
-
-    slim_index = 0
-    sub_slim_index = 0
-
-    for y in range(mask_2d.shape[0]):
-        for x in range(mask_2d.shape[1]):
-            if not mask_2d[y, x]:
-                sub = sub_size[slim_index]
-
-                for y1 in range(sub):
-                    for x1 in range(sub):
-                        sub_native_index_for_sub_slim_index_2d[sub_slim_index, :] = (
-                            (y * sub) + y1,
-                            (x * sub) + x1,
-                        )
-                        sub_slim_index += 1
-
-                slim_index += 1
-
-    return sub_native_index_for_sub_slim_index_2d
-
-
 @numba_util.jit()
 def slim_index_for_sub_slim_index_via_mask_2d_from(
     mask_2d: np.ndarray, sub_size: np.ndarray

autoarray/operators/over_sampling/over_sampler.py

@@ -128,7 +128,7 @@ def __init__(self, mask: Mask2D, sub_size: Union[int, Array2D]):
         based on the sub-grid sizes.
 
         The over sampling class has functions dedicated to mapping between the sub-grid and pixel-grid, for example
-        `sub_mask_native_for_sub_mask_slim` and `slim_for_sub_slim`.
+        `slim_for_sub_slim`.
 
          The class `OverSampling` is used for the high level API, whereby this is where users input their
          preferred over-sampling configuration. This class, `OverSampler`, contains the functionality
@@ -258,63 +258,6 @@ def binned_array_2d_from(self, array: Array2D) -> "Array2D":
             mask=self.mask,
         )
 
-    @cached_property
-    def sub_mask_native_for_sub_mask_slim(self) -> np.ndarray:
-        """
-        Derives a 1D ``ndarray`` which maps every subgridded 1D ``slim`` index of the ``Mask2D`` to its
-        subgridded 2D ``native`` index.
-
-        For example, for the following ``Mask2D`` for ``sub_size=1``:
-
-        ::
-            [[True,  True,  True, True]
-             [True, False, False, True],
-             [True, False,  True, True],
-             [True,  True,  True, True]]
-
-        This has three unmasked (``False`` values) which have the ``slim`` indexes:
-
-        ::
-            [0, 1, 2]
-
-        The array ``sub_mask_native_for_sub_mask_slim`` is therefore:
-
-        ::
-            [[1,1], [1,2], [2,1]]
-
-        For a ``Mask2D`` with ``sub_size=2`` each unmasked ``False`` entry is split into a sub-pixel of size 2x2 and
-        there are therefore 12 ``slim`` indexes:
-
-        ::
-            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
-
-        The array ``native_for_slim`` is therefore:
-
-        ::
-            [[2,2], [2,3], [2,4], [2,5], [3,2], [3,3], [3,4], [3,5], [4,2], [4,3], [5,2], [5,3]]
-
-        Examples
-        --------
-
-        .. code-block:: python
-
-            import autoarray as aa
-
-            mask_2d = aa.Mask2D(
-                mask=[[True,  True,  True, True]
-                      [True, False, False, True],
-                      [True, False,  True, True],
-                      [True,  True,  True, True]]
-                pixel_scales=1.0,
-            )
-
-            derive_indexes_2d = aa.DeriveIndexes2D(mask=mask_2d)
-
-            print(derive_indexes_2d.sub_mask_native_for_sub_mask_slim)
-        """
-        return over_sample_util.native_sub_index_for_slim_sub_index_2d_from(
-            mask_2d=self.mask.array, sub_size=self.sub_size.array
-        ).astype("int")
 
     @cached_property
     def slim_for_sub_slim(self) -> np.ndarray:

autoarray/structures/arrays/array_2d_util.py

@@ -6,7 +6,6 @@
 if TYPE_CHECKING:
     from autoarray.mask.mask_2d import Mask2D
 
-from autoarray import numba_util
 from autoarray.mask import mask_2d_util
 
 from autoarray import exc