numpy.ma.notmasked_contiguous

numpy.ma.notmasked_contiguous(a, axis=None) [source]

Find contiguous unmasked data in a masked array along the given axis.

Parameters:	a : array_like The input array. axis : int, optional Axis along which to perform the operation. If None (default), applies to a flattened version of the array, and this is the same as flatnotmasked_contiguous.
Returns:	endpoints : list A list of slices (start and end indexes) of unmasked indexes in the array. If the input is 2d and axis is specified, the result is a list of lists.

See also

flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges, clump_masked, clump_unmasked

Notes

Only accepts 2-D arrays at most.

Examples

>>> a = np.arange(12).reshape((3, 4))
>>> mask = np.zeros_like(a)
>>> mask[1:, :-1] = 1; mask[0, 1] = 1; mask[-1, 0] = 0
>>> ma = np.ma.array(a, mask=mask)
>>> ma
masked_array(
  data=[[0, --, 2, 3],
        [--, --, --, 7],
        [8, --, --, 11]],
  mask=[[False,  True, False, False],
        [ True,  True,  True, False],
        [False,  True,  True, False]],
  fill_value=999999)
>>> np.array(ma[~ma.mask])
array([ 0,  2,  3,  7, 8, 11])

>>> np.ma.notmasked_contiguous(ma)
[slice(0, 1, None), slice(2, 4, None), slice(7, 9, None), slice(11, 12, None)]

>>> np.ma.notmasked_contiguous(ma, axis=0)
[[slice(0, 1, None), slice(2, 3, None)],  # column broken into two segments
 [],                                      # fully masked column
 [slice(0, 1, None)],
 [slice(0, 3, None)]]

>>> np.ma.notmasked_contiguous(ma, axis=1)
[[slice(0, 1, None), slice(2, 4, None)],  # row broken into two segments
 [slice(3, 4, None)],
 [slice(0, 1, None), slice(3, 4, None)]]