fromflex(fxarray)
The input array has to have a data-type with _data
and _mask
fields. This type of array is output by MaskedArray.toflex
.
The structured input array, containing _data
and _mask
fields. If present, other fields are discarded.
The constructed masked array.
Build a masked array from a suitable flexible-type array.
MaskedArray.toflex
Build a flexible-type array from a masked array.
>>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[0] + [1, 0] * 4)This example is valid syntax, but we were not able to check execution
... rec = x.toflex()
... rec array([[(0, False), (1, True), (2, False)], [(3, True), (4, False), (5, True)], [(6, False), (7, True), (8, False)]], dtype=[('_data', '<i8'), ('_mask', '?')])
>>> x2 = np.ma.fromflex(rec)
... x2 masked_array( data=[[0, --, 2], [--, 4, --], [6, --, 8]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=999999)
Extra fields can be present in the structured array but are discarded:
This example is valid syntax, but we were not able to check execution>>> dt = [('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')]This example is valid syntax, but we were not able to check execution
... rec2 = np.zeros((2, 2), dtype=dt)
... rec2 array([[(0, False, 0.), (0, False, 0.)], [(0, False, 0.), (0, False, 0.)]], dtype=[('_data', '<i4'), ('_mask', '?'), ('field3', '<f4')])
>>> y = np.ma.fromflex(rec2)See :
... y masked_array( data=[[0, 0], [0, 0]], mask=[[False, False], [False, False]], fill_value=999999, dtype=int32)
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