diff options
| -rw-r--r-- | doc/release/upcoming_changes/23322.improvement.rst | 4 | ||||
| -rw-r--r-- | numpy/ma/core.py | 112 | ||||
| -rw-r--r-- | numpy/ma/extras.py | 92 | ||||
| -rw-r--r-- | numpy/ma/tests/test_extras.py | 41 |
4 files changed, 153 insertions, 96 deletions
diff --git a/doc/release/upcoming_changes/23322.improvement.rst b/doc/release/upcoming_changes/23322.improvement.rst new file mode 100644 index 000000000..ce5ab8cf5 --- /dev/null +++ b/doc/release/upcoming_changes/23322.improvement.rst @@ -0,0 +1,4 @@ +`np.ma.dot()` now supports for non-2d arrays +-------------------------------------------- +Previously `np.ma.dot()` only worked if `a` and `b` were both 2d. +Now it works for non-2d arrays as well as `np.dot()`. diff --git a/numpy/ma/core.py b/numpy/ma/core.py index fcc321a73..dcec82773 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -7110,7 +7110,7 @@ def diag(v, k=0): Examples -------- - + Create an array with negative values masked: >>> import numpy as np @@ -7521,7 +7521,7 @@ def diff(a, /, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue): if len(combined) > 1: a = np.ma.concatenate(combined, axis) - # GH 22465 np.diff without prepend/append preserves the mask + # GH 22465 np.diff without prepend/append preserves the mask return np.diff(a, n, axis) @@ -7752,94 +7752,18 @@ def round_(a, decimals=0, out=None): round = round_ -# Needed by dot, so move here from extras.py. It will still be exported -# from extras.py for compatibility. -def mask_rowcols(a, axis=None): +def _mask_propagate(a, axis): """ - Mask rows and/or columns of a 2D array that contain masked values. - - Mask whole rows and/or columns of a 2D array that contain - masked values. The masking behavior is selected using the - `axis` parameter. - - - If `axis` is None, rows *and* columns are masked. - - If `axis` is 0, only rows are masked. - - If `axis` is 1 or -1, only columns are masked. - - Parameters - ---------- - a : array_like, MaskedArray - The array to mask. If not a MaskedArray instance (or if no array - elements are masked). The result is a MaskedArray with `mask` set - to `nomask` (False). Must be a 2D array. - axis : int, optional - Axis along which to perform the operation. If None, applies to a - flattened version of the array. - - Returns - ------- - a : MaskedArray - A modified version of the input array, masked depending on the value - of the `axis` parameter. - - Raises - ------ - NotImplementedError - If input array `a` is not 2D. - - See Also - -------- - mask_rows : Mask rows of a 2D array that contain masked values. - mask_cols : Mask cols of a 2D array that contain masked values. - masked_where : Mask where a condition is met. - - Notes - ----- - The input array's mask is modified by this function. - - Examples - -------- - >>> import numpy.ma as ma - >>> a = np.zeros((3, 3), dtype=int) - >>> a[1, 1] = 1 - >>> a - array([[0, 0, 0], - [0, 1, 0], - [0, 0, 0]]) - >>> a = ma.masked_equal(a, 1) - >>> a - masked_array( - data=[[0, 0, 0], - [0, --, 0], - [0, 0, 0]], - mask=[[False, False, False], - [False, True, False], - [False, False, False]], - fill_value=1) - >>> ma.mask_rowcols(a) - masked_array( - data=[[0, --, 0], - [--, --, --], - [0, --, 0]], - mask=[[False, True, False], - [ True, True, True], - [False, True, False]], - fill_value=1) - + Mask whole 1-d vectors of an array that contain masked values. """ a = array(a, subok=False) - if a.ndim != 2: - raise NotImplementedError("mask_rowcols works for 2D arrays only.") m = getmask(a) - # Nothing is masked: return a - if m is nomask or not m.any(): + if m is nomask or not m.any() or axis is None: return a - maskedval = m.nonzero() a._mask = a._mask.copy() - if not axis: - a[np.unique(maskedval[0])] = masked - if axis in [None, 1, -1]: - a[:, np.unique(maskedval[1])] = masked + axes = normalize_axis_tuple(axis, a.ndim) + for ax in axes: + a._mask |= m.any(axis=ax, keepdims=True) return a @@ -7856,10 +7780,6 @@ def dot(a, b, strict=False, out=None): corresponding method, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory. - .. note:: - Works only with 2-D arrays at the moment. - - Parameters ---------- a, b : masked_array_like @@ -7903,18 +7823,22 @@ def dot(a, b, strict=False, out=None): fill_value=999999) """ - # !!!: Works only with 2D arrays. There should be a way to get it to run - # with higher dimension - if strict and (a.ndim == 2) and (b.ndim == 2): - a = mask_rowcols(a, 0) - b = mask_rowcols(b, 1) + if strict is True: + if np.ndim(a) == 0 or np.ndim(b) == 0: + pass + elif b.ndim == 1: + a = _mask_propagate(a, a.ndim - 1) + b = _mask_propagate(b, b.ndim - 1) + else: + a = _mask_propagate(a, a.ndim - 1) + b = _mask_propagate(b, b.ndim - 2) am = ~getmaskarray(a) bm = ~getmaskarray(b) if out is None: d = np.dot(filled(a, 0), filled(b, 0)) m = ~np.dot(am, bm) - if d.ndim == 0: + if np.ndim(d) == 0: d = np.asarray(d) r = d.view(get_masked_subclass(a, b)) r.__setmask__(m) diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py index 4abe2107a..8a6246c36 100644 --- a/numpy/ma/extras.py +++ b/numpy/ma/extras.py @@ -27,8 +27,7 @@ from . import core as ma from .core import ( MaskedArray, MAError, add, array, asarray, concatenate, filled, count, getmask, getmaskarray, make_mask_descr, masked, masked_array, mask_or, - nomask, ones, sort, zeros, getdata, get_masked_subclass, dot, - mask_rowcols + nomask, ones, sort, zeros, getdata, get_masked_subclass, dot ) import numpy as np @@ -955,6 +954,95 @@ def compress_cols(a): return compress_rowcols(a, 1) +def mask_rowcols(a, axis=None): + """ + Mask rows and/or columns of a 2D array that contain masked values. + + Mask whole rows and/or columns of a 2D array that contain + masked values. The masking behavior is selected using the + `axis` parameter. + + - If `axis` is None, rows *and* columns are masked. + - If `axis` is 0, only rows are masked. + - If `axis` is 1 or -1, only columns are masked. + + Parameters + ---------- + a : array_like, MaskedArray + The array to mask. If not a MaskedArray instance (or if no array + elements are masked), the result is a MaskedArray with `mask` set + to `nomask` (False). Must be a 2D array. + axis : int, optional + Axis along which to perform the operation. If None, applies to a + flattened version of the array. + + Returns + ------- + a : MaskedArray + A modified version of the input array, masked depending on the value + of the `axis` parameter. + + Raises + ------ + NotImplementedError + If input array `a` is not 2D. + + See Also + -------- + mask_rows : Mask rows of a 2D array that contain masked values. + mask_cols : Mask cols of a 2D array that contain masked values. + masked_where : Mask where a condition is met. + + Notes + ----- + The input array's mask is modified by this function. + + Examples + -------- + >>> import numpy.ma as ma + >>> a = np.zeros((3, 3), dtype=int) + >>> a[1, 1] = 1 + >>> a + array([[0, 0, 0], + [0, 1, 0], + [0, 0, 0]]) + >>> a = ma.masked_equal(a, 1) + >>> a + masked_array( + data=[[0, 0, 0], + [0, --, 0], + [0, 0, 0]], + mask=[[False, False, False], + [False, True, False], + [False, False, False]], + fill_value=1) + >>> ma.mask_rowcols(a) + masked_array( + data=[[0, --, 0], + [--, --, --], + [0, --, 0]], + mask=[[False, True, False], + [ True, True, True], + [False, True, False]], + fill_value=1) + + """ + a = array(a, subok=False) + if a.ndim != 2: + raise NotImplementedError("mask_rowcols works for 2D arrays only.") + m = getmask(a) + # Nothing is masked: return a + if m is nomask or not m.any(): + return a + maskedval = m.nonzero() + a._mask = a._mask.copy() + if not axis: + a[np.unique(maskedval[0])] = masked + if axis in [None, 1, -1]: + a[:, np.unique(maskedval[1])] = masked + return a + + def mask_rows(a, axis=np._NoValue): """ Mask rows of a 2D array that contain masked values. diff --git a/numpy/ma/tests/test_extras.py b/numpy/ma/tests/test_extras.py index e59ba3656..d09a50fec 100644 --- a/numpy/ma/tests/test_extras.py +++ b/numpy/ma/tests/test_extras.py @@ -730,6 +730,47 @@ class TestCompressFunctions: assert_equal(c.mask, [[0, 0, 1], [1, 1, 1], [0, 0, 1]]) c = dot(b, a, strict=False) assert_equal(c, np.dot(b.filled(0), a.filled(0))) + # + a = masked_array(np.arange(8).reshape(2, 2, 2), + mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]]) + b = masked_array(np.arange(8).reshape(2, 2, 2), + mask=[[[0, 0], [0, 0]], [[0, 0], [0, 1]]]) + c = dot(a, b, strict=True) + assert_equal(c.mask, + [[[[1, 1], [1, 1]], [[0, 0], [0, 1]]], + [[[0, 0], [0, 1]], [[0, 0], [0, 1]]]]) + c = dot(a, b, strict=False) + assert_equal(c.mask, + [[[[0, 0], [0, 1]], [[0, 0], [0, 0]]], + [[[0, 0], [0, 0]], [[0, 0], [0, 0]]]]) + c = dot(b, a, strict=True) + assert_equal(c.mask, + [[[[1, 0], [0, 0]], [[1, 0], [0, 0]]], + [[[1, 0], [0, 0]], [[1, 1], [1, 1]]]]) + c = dot(b, a, strict=False) + assert_equal(c.mask, + [[[[0, 0], [0, 0]], [[0, 0], [0, 0]]], + [[[0, 0], [0, 0]], [[1, 0], [0, 0]]]]) + # + a = masked_array(np.arange(8).reshape(2, 2, 2), + mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]]) + b = 5. + c = dot(a, b, strict=True) + assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]]) + c = dot(a, b, strict=False) + assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]]) + c = dot(b, a, strict=True) + assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]]) + c = dot(b, a, strict=False) + assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]]) + # + a = masked_array(np.arange(8).reshape(2, 2, 2), + mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]]) + b = masked_array(np.arange(2), mask=[0, 1]) + c = dot(a, b, strict=True) + assert_equal(c.mask, [[1, 1], [1, 1]]) + c = dot(a, b, strict=False) + assert_equal(c.mask, [[1, 0], [0, 0]]) def test_dot_returns_maskedarray(self): # See gh-6611 |