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Showing content from https://docs.scipy.org/doc/numpy/reference/generated/numpy.ma.getmask.html below:

numpy.ma.getmask — NumPy v2.3 Manual

ma.getmask(a)[source]#

Return the mask of a masked array, or nomask.

Return the mask of a as an ndarray if a is a MaskedArray and the mask is not nomask, else return nomask. To guarantee a full array of booleans of the same shape as a, use getmaskarray.

Parameters:

aarray_like

Input MaskedArray for which the mask is required.

See also

getdata

Return the data of a masked array as an ndarray.

getmaskarray

Return the mask of a masked array, or full array of False.

Examples

>>> import numpy as np
>>> import numpy.ma as ma
>>> a = ma.masked_equal([[1,2],[3,4]], 2)
>>> a
masked_array(
  data=[[1, --],
        [3, 4]],
  mask=[[False,  True],
        [False, False]],
  fill_value=2)
>>> ma.getmask(a)
array([[False,  True],
       [False, False]])

Equivalently use the MaskedArray mask attribute.

>>> a.mask
array([[False,  True],
       [False, False]])

Result when mask == nomask

>>> b = ma.masked_array([[1,2],[3,4]])
>>> b
masked_array(
  data=[[1, 2],
        [3, 4]],
  mask=False,
  fill_value=999999)
>>> ma.nomask
False
>>> ma.getmask(b) == ma.nomask
True
>>> b.mask == ma.nomask
True

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