mask_or(m1, m2, copy=False, shrink=True)

The result may be a view on :None:None:`m1` or :None:None:`m2` if the other is :None:None:`nomask` (i.e. False).

Parameters

m1, m2 : array_like

Input masks.

copy : bool, optional

If copy is False and one of the inputs is :None:None:`nomask`, return a view of the other input mask. Defaults to False.

shrink : bool, optional

Whether to shrink the output to :None:None:`nomask` if all its values are False. Defaults to True.

Raises

ValueError

If :None:None:`m1` and :None:None:`m2` have different flexible dtypes.

Returns

mask : output mask

The result masks values that are masked in either :None:None:`m1` or :None:None:`m2`.

Combine two masks with the logical_or operator.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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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GitHub : /numpy/ma/core.py#1697
type: <class 'function'>
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