numpy.ma.mask_or#

ma.mask_or(m1, m2, copy=False, shrink=True)[source]#

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, m2array_like

Input masks.

copybool, optional

If copy is False and one of the inputs is nomask, return a view of the other input mask. Defaults to False.

shrinkbool, optional

Whether to shrink the output to nomask if all its values are False. Defaults to True.

Returns:

maskoutput mask

The result masks values that are masked in either m1 or m2.

Raises:

ValueError

If m1 and m2 have different flexible dtypes.

Examples

Try it in your browser!

>>> import numpy as np
>>> 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])

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