Modified ndfilters.mean_filter() to use `ndfilters.generic_filter()…

…`. (#18)

ndfilters/_mean.py

@@ -1,20 +1,23 @@
+from typing import Literal
 import numpy as np
 import numba
+import astropy.units as u
+import ndfilters
 
 __all__ = [
     "mean_filter",
 ]
 
 
 def mean_filter(
-    array: np.ndarray,
+    array: np.ndarray | u.Quantity,
     size: int | tuple[int, ...],
     axis: None | int | tuple[int, ...] = None,
     where: bool | np.ndarray = True,
+    mode: Literal["mirror", "nearest", "wrap", "truncate"] = "mirror",
 ) -> np.ndarray:
     """
     Calculate a multidimensional rolling mean.
-    The kernel is truncated at the edges of the array.
 
     Parameters
     ----------
@@ -23,14 +26,21 @@ def mean_filter(
     size
         The shape of the kernel over which the mean will be calculated.
     axis
-        The axes over which to apply the kernel. If :obj:`None` the kernel
-        is applied to every axis.
+        The axes over which to apply the kernel.
+        Should either be a scalar or have the same number of items as `size`.
+        If :obj:`None` (the default) the kernel spans every axis of the array.
     where
-        A boolean mask used to select which elements of the input array to filter.
+        An optional mask that can be used to exclude parts of the array during
+        filtering.
+    mode
+        The method used to extend the input array beyond its boundaries.
+        See :func:`scipy.ndimage.generic_filter` for the definitions.
+        Currently, only "mirror", "nearest", "wrap", and "truncate" modes are
+        supported.
 
     Returns
     -------
-        A copy of the array with a mean filter applied.
+        A copy of the array with the mean filter applied.
 
     Examples
     --------
@@ -47,92 +57,23 @@ def mean_filter(
         fig, axs = plt.subplots(ncols=2, sharex=True, sharey=True)
         axs[0].set_title("original image");
         axs[0].imshow(img, cmap="gray");
-        axs[1].set_title("mean filtered image");
+        axs[1].set_title("filtered image");
         axs[1].imshow(img_filtered, cmap="gray");
-    """
-    array, where = np.broadcast_arrays(array, where, subok=True)
-
-    if axis is None:
-        axis = tuple(range(array.ndim))
-    else:
-        axis = np.core.numeric.normalize_axis_tuple(axis=axis, ndim=array.ndim)
-
-    if isinstance(size, int):
-        size = (size,) * len(axis)
-
-    result = array
-    for sz, ax in zip(size, axis, strict=True):
-        result = _mean_filter_1d(
-            array=result,
-            size=sz,
-            axis=ax,
-            where=where,
-        )
-
-    return result
-
 
-def _mean_filter_1d(
-    array: np.ndarray,
-    size: int,
-    axis: int,
-    where: np.ndarray,
-) -> np.ndarray:
-
-    array = np.moveaxis(array, axis, ~0)
-    where = np.moveaxis(where, axis, ~0)
-
-    shape = array.shape
-
-    array = array.reshape(-1, shape[~0])
-    where = where.reshape(-1, shape[~0])
-
-    result = _mean_filter_1d_numba(
+    """
+    return ndfilters.generic_filter(
         array=array,
+        function=_mean,
         size=size,
+        axis=axis,
         where=where,
-        out=np.empty_like(array),
+        mode=mode,
     )
 
-    result = result.reshape(shape)
 
-    result = np.moveaxis(result, ~0, axis)
-
-    return result
-
-
-@numba.njit(parallel=True, cache=True)
-def _mean_filter_1d_numba(
+@numba.njit
+def _mean(
     array: np.ndarray,
-    size: int,
-    where: np.ndarray,
-    out: np.ndarray,
-) -> np.ndarray:
-
-    num_t, num_x = array.shape
-
-    halfsize = size // 2
-
-    for t in numba.prange(num_t):
-
-        for i in range(num_x):
-
-            sum = 0
-            count = 0
-            for j in range(size):
-
-                j2 = j - halfsize
-
-                k = i + j2
-                if k < 0:
-                    continue
-                elif k >= num_x:
-                    continue
-
-                if where[t, k]:
-                    sum += array[t, k]
-                    count += 1
-
-            out[t, i] = sum / count
-
-    return out
+    args: tuple[float],
+) -> float:
+    return np.mean(array)

ndfilters/_tests/test_mean.py

@@ -1,3 +1,4 @@
+from typing import Literal
 import pytest
 import numpy as np
 import scipy.ndimage
@@ -32,15 +33,25 @@
         (2, 1, 0),
     ],
 )
+@pytest.mark.parametrize(
+    argnames="mode",
+    argvalues=[
+        "mirror",
+        "nearest",
+        "wrap",
+    ],
+)
 def test_mean_filter(
     array: np.ndarray,
     size: int | tuple[int, ...],
     axis: None | int | tuple[int, ...],
+    mode: Literal["mirror", "nearest", "wrap", "truncate"],
 ):
     kwargs = dict(
         array=array,
         size=size,
         axis=axis,
+        mode=mode,
     )
 
     if axis is None:
@@ -74,11 +85,7 @@ def test_mean_filter(
     expected = scipy.ndimage.uniform_filter(
         input=array,
         size=size_scipy,
-        mode="constant",
-    ) / scipy.ndimage.uniform_filter(
-        input=np.ones(array.shape),
-        size=size_scipy,
-        mode="constant",
+        mode=mode,
     )
 
     if isinstance(result, u.Quantity):