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Diffstat (limited to 'numpy/ma/extras.py')
| -rw-r--r-- | numpy/ma/extras.py | 188 |
1 files changed, 167 insertions, 21 deletions
diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py index 0f0d504c5..5804cf3c1 100644 --- a/numpy/ma/extras.py +++ b/numpy/ma/extras.py @@ -1473,6 +1473,12 @@ class mr_class(MAxisConcatenator): """ Translate slice objects to concatenation along the first axis. + This is the masked array version of `lib.index_tricks.RClass`. + + See Also + -------- + lib.index_tricks.RClass + Examples -------- >>> np.ma.mr_[np.ma.array([1,2,3]), 0, 0, np.ma.array([4,5,6])] @@ -1490,8 +1496,44 @@ mr_ = mr_class() def flatnotmasked_edges(a): """ - Find the indices of the first and last valid values in a 1D masked array. - If all values are masked, returns None. + Find the indices of the first and last unmasked values. + + Expects a 1-D `MaskedArray`, returns None if all values are masked. + + Parameters + ---------- + arr : array_like + Input 1-D `MaskedArray` + + Returns + ------- + edges : ndarray or None + The indices of first and last non-masked value in the array. + Returns None if all values are masked. + + See Also + -------- + flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges + + Notes + ----- + Only accepts 1-D arrays. + + Examples + -------- + >>> a = np.arange(10) + >>> mask = (a < 3) | (a > 8) | (a == 5) + + >>> ma = np.ma.array(a, mask=m) + >>> np.array(ma[~ma.mask]) + array([3, 4, 6, 7, 8]) + + >>> flatnotmasked_edges(ma) + array([3, 8]) + + >>> ma = np.ma.array(a, mask=np.ones_like(a)) + >>> print flatnotmasked_edges(ma) + None """ m = getmask(a) @@ -1506,18 +1548,43 @@ def flatnotmasked_edges(a): def notmasked_edges(a, axis=None): """ - Find the indices of the first and last not masked values along - the given axis in a masked array. + Find the indices of the first and last unmasked values along an axis. If all values are masked, return None. Otherwise, return a list - of 2 tuples, corresponding to the indices of the first and last + of two tuples, corresponding to the indices of the first and last unmasked values respectively. Parameters ---------- + a : array_like + The input array. axis : int, optional Axis along which to perform the operation. - If None, applies to a flattened version of the array. + If None (default), applies to a flattened version of the array. + + Returns + ------- + edges : ndarray or list + An array of start and end indexes if there are any masked data in + the array. If there are no masked data in the array, `edges` is a + list of the first and last index. + + See Also + -------- + flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous + + Examples + -------- + >>> a = np.arange(9).reshape((3, 3)) + >>> m = np.zeros_like(a) + >>> m[1:, 1:] = 1 + + >>> ma = np.ma.array(a, mask=m) + >>> np.array(ma[~ma.mask]) + array([0, 1, 2, 3, 6]) + + >>> np.ma.extras.notmasked_edges(ma) + array([0, 6]) """ a = asarray(a) @@ -1531,9 +1598,39 @@ def notmasked_edges(a, axis=None): def flatnotmasked_contiguous(a): """ - Find contiguous unmasked data in a flattened masked array. + Find contiguous unmasked data in a masked array along the given axis. - Return a sorted sequence of slices (start index, end index). + Parameters + ---------- + a : narray + The input array. + + Returns + ------- + slice_list : list + A sorted sequence of slices (start index, end index). + + See Also + -------- + flatnotmasked_edges, notmasked_contiguous, notmasked_edges + + Notes + ----- + Only accepts 2-D arrays at most. + + Examples + -------- + >>> a = np.arange(10) + >>> mask = (a < 3) | (a > 8) | (a == 5) + >>> ma = np.ma.array(a, mask=mask) + >>> np.array(ma[~ma.mask]) + array([3, 4, 6, 7, 8]) + + >>> np.ma.extras.flatnotmasked_contiguous(ma) + [slice(3, 4, None), slice(6, 8, None)] + >>> ma = np.ma.array(a, mask=np.ones_like(a)) + >>> print np.ma.extras.flatnotmasked_edges(ma) + None """ m = getmask(a) @@ -1556,17 +1653,38 @@ def notmasked_contiguous(a, axis=None): Parameters ---------- + a : array_like + The input array. axis : int, optional Axis along which to perform the operation. - If None, applies to a flattened version of the array. + If None (default), applies to a flattened version of the array. Returns ------- - A sorted sequence of slices (start index, end index). + endpoints : list + A list of slices (start and end indexes) of unmasked indexes + in the array. + + See Also + -------- + flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges Notes ----- - Only accepts 2D arrays at most. + Only accepts 2-D arrays at most. + + Examples + -------- + >>> a = np.arange(9).reshape((3, 3)) + >>> mask = np.zeros_like(a) + >>> mask[1:, 1:] = 1 + + >>> ma = np.ma.array(a, mask=mask) + >>> np.array(ma[~ma.mask]) + array([0, 1, 2, 3, 6]) + + >>> np.ma.extras.notmasked_contiguous(ma) + [slice(0, 3, None), slice(6, 6, None)] """ a = asarray(a) @@ -1607,16 +1725,30 @@ def _ezclump(mask): def clump_unmasked(a): """ - Returns a list of slices corresponding to the unmasked clumps of a 1D array. + Return list of slices corresponding to the unmasked clumps of a 1-D array. + + Parameters + ---------- + a : ndarray + A one-dimensional masked array. + + Returns + ------- + slices : list of slice + The list of slices, one for each continuous region of unmasked + elements in `a`. + + Notes + ----- + .. versionadded:: 1.4.0 Examples -------- - >>> a = ma.masked_array(np.arange(10)) - >>> a[[0, 1, 2, 6, 8, 9]] = ma.masked - >>> clump_unmasked(a) + >>> a = np.ma.masked_array(np.arange(10)) + >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked + >>> np.ma.extras.clump_unmasked(a) [slice(3, 6, None), slice(7, 8, None)] - .. versionadded:: 1.4.0 """ mask = getattr(a, '_mask', nomask) if mask is nomask: @@ -1631,16 +1763,30 @@ def clump_unmasked(a): def clump_masked(a): """ - Returns a list of slices corresponding to the masked clumps of a 1D array. + Returns a list of slices corresponding to the masked clumps of a 1-D array. + + Parameters + ---------- + a : ndarray + A one-dimensional masked array. + + Returns + ------- + slices : list of slice + The list of slices, one for each continuous region of masked elements + in `a`. + + Notes + ----- + .. versionadded:: 1.4.0 Examples -------- - >>> a = ma.masked_array(np.arange(10)) - >>> a[[0, 1, 2, 6, 8, 9]] = ma.masked - >>> clump_masked(a) + >>> a = np.ma.masked_array(np.arange(10)) + >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked + >>> np.ma.extras.clump_masked(a) [slice(0, 3, None), slice(6, 7, None), slice(8, None, None)] - .. versionadded:: 1.4.0 """ mask = ma.getmask(a) if mask is nomask: |