notmasked_contiguous(a, axis=None)

Notes

Only accepts 2-D arrays at most.

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.

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

See Also

clump_masked

clump_unmasked

flatnotmasked_contiguous

flatnotmasked_edges

notmasked_edges

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)], [], [slice(0, 1, None)], [slice(0, 3, None)]]

>>> np.ma.notmasked_contiguous(ma, axis=1)
[[slice(0, 1, None), slice(2, 4, None)], [slice(3, 4, None)], [slice(0, 1, None), slice(3, 4, None)]]

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.ma.extras.clump_unmasked numpy.ma.extras.notmasked_edges numpy.ma.extras.flatnotmasked_contiguous numpy.ma.extras.clump_masked numpy.ma.extras.flatnotmasked_edges

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