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# numpy.ma.mask_or

`numpy.ma.``mask_or`(m1, m2, copy=False, shrink=True)

  Combine two masks with the `logical_or` operator.

  The result may be a view on m1 or m2 if the other is `nomask` (i.e. False).

  Parameters: | m1, m2 : array_like Input masks. copy : bool, optional If copy is False and one of the inputs is `nomask`, return a view of the other input mask. Defaults to False. shrink : bool, optional Whether to shrink the output to `nomask` if all its values are False. Defaults to True.
  Returns: | mask : output mask The result masks values that are masked in either m1 or m2.
  Raises: | ValueError If m1 and m2 have different flexible dtypes.

  Examples

      >>> m1 = np.ma.make_mask([0, 1, 1, 0])
      >>> m2 = np.ma.make_mask([1, 0, 0, 0])
      >>> np.ma.mask_or(m1, m2)
      array([ True,  True,  True, False], dtype=bool)