numpy.ma.make_mask

numpy.ma.make_mask(m, copy=False, shrink=True, dtype=<class 'numpy.bool_'>)

Create a boolean mask from an array.

Return m as a boolean mask, creating a copy if necessary or requested. The function can accept any sequence that is convertible to integers, or nomask. Does not require that contents must be 0s and 1s, values of 0 are interepreted as False, everything else as True.

Parameters:

m : array_like

Potential mask.

copy : bool, optional

Whether to return a copy of m (True) or m itself (False).

shrink : bool, optional

Whether to shrink m to nomask if all its values are False.

dtype : dtype, optional

Data-type of the output mask. By default, the output mask has a dtype of MaskType (bool). If the dtype is flexible, each field has a boolean dtype. This is ignored when m is nomask, in which case nomask is always returned.

Returns:

result : ndarray

A boolean mask derived from m.

Examples

>>> import numpy.ma as ma
>>> m = [True, False, True, True]
>>> ma.make_mask(m)
array([ True, False,  True,  True])
>>> m = [1, 0, 1, 1]
>>> ma.make_mask(m)
array([ True, False,  True,  True])
>>> m = [1, 0, 2, -3]
>>> ma.make_mask(m)
array([ True, False,  True,  True])

Effect of the shrink parameter.

>>> m = np.zeros(4)
>>> m
array([0., 0., 0., 0.])
>>> ma.make_mask(m)
False
>>> ma.make_mask(m, shrink=False)
array([False, False, False, False])

Using a flexible dtype.

>>> m = [1, 0, 1, 1]
>>> n = [0, 1, 0, 0]
>>> arr = []
>>> for man, mouse in zip(m, n):
...     arr.append((man, mouse))
>>> arr
[(1, 0), (0, 1), (1, 0), (1, 0)]
>>> dtype = np.dtype({'names':['man', 'mouse'],
...                   'formats':[np.int64, np.int64]})
>>> arr = np.array(arr, dtype=dtype)
>>> arr
array([(1, 0), (0, 1), (1, 0), (1, 0)],
      dtype=[('man', '<i8'), ('mouse', '<i8')])
>>> ma.make_mask(arr, dtype=dtype)
array([(True, False), (False, True), (True, False), (True, False)],
      dtype=[('man', '|b1'), ('mouse', '|b1')])