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 `n
(m1, m2, copy=False, shrink=True)
| 1702 | |
| 1703 | |
| 1704 | def mask_or(m1, m2, copy=False, shrink=True): |
| 1705 | """ |
| 1706 | Combine two masks with the ``logical_or`` operator. |
| 1707 | |
| 1708 | The result may be a view on `m1` or `m2` if the other is `nomask` |
| 1709 | (i.e. False). |
| 1710 | |
| 1711 | Parameters |
| 1712 | ---------- |
| 1713 | m1, m2 : array_like |
| 1714 | Input masks. |
| 1715 | copy : bool, optional |
| 1716 | If copy is False and one of the inputs is `nomask`, return a view |
| 1717 | of the other input mask. Defaults to False. |
| 1718 | shrink : bool, optional |
| 1719 | Whether to shrink the output to `nomask` if all its values are |
| 1720 | False. Defaults to True. |
| 1721 | |
| 1722 | Returns |
| 1723 | ------- |
| 1724 | mask : output mask |
| 1725 | The result masks values that are masked in either `m1` or `m2`. |
| 1726 | |
| 1727 | Raises |
| 1728 | ------ |
| 1729 | ValueError |
| 1730 | If `m1` and `m2` have different flexible dtypes. |
| 1731 | |
| 1732 | Examples |
| 1733 | -------- |
| 1734 | >>> m1 = np.ma.make_mask([0, 1, 1, 0]) |
| 1735 | >>> m2 = np.ma.make_mask([1, 0, 0, 0]) |
| 1736 | >>> np.ma.mask_or(m1, m2) |
| 1737 | array([ True, True, True, False]) |
| 1738 | |
| 1739 | """ |
| 1740 | |
| 1741 | if (m1 is nomask) or (m1 is False): |
| 1742 | dtype = getattr(m2, 'dtype', MaskType) |
| 1743 | return make_mask(m2, copy=copy, shrink=shrink, dtype=dtype) |
| 1744 | if (m2 is nomask) or (m2 is False): |
| 1745 | dtype = getattr(m1, 'dtype', MaskType) |
| 1746 | return make_mask(m1, copy=copy, shrink=shrink, dtype=dtype) |
| 1747 | if m1 is m2 and is_mask(m1): |
| 1748 | return m1 |
| 1749 | (dtype1, dtype2) = (getattr(m1, 'dtype', None), getattr(m2, 'dtype', None)) |
| 1750 | if dtype1 != dtype2: |
| 1751 | raise ValueError("Incompatible dtypes '%s'<>'%s'" % (dtype1, dtype2)) |
| 1752 | if dtype1.names is not None: |
| 1753 | # Allocate an output mask array with the properly broadcast shape. |
| 1754 | newmask = np.empty(np.broadcast(m1, m2).shape, dtype1) |
| 1755 | _recursive_mask_or(m1, m2, newmask) |
| 1756 | return newmask |
| 1757 | return make_mask(umath.logical_or(m1, m2), copy=copy, shrink=shrink) |
| 1758 | |
| 1759 | |
| 1760 | def flatten_mask(mask): |