numpy.ma.make_mask(m, copy=False, shrink=True, dtype=)
[source]
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 shrink : bool, optional Whether to shrink 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 |
---|---|
Returns: |
result : ndarray A boolean mask derived from |
>>> import numpy.ma as ma >>> m = [True, False, True, True] >>> ma.make_mask(m) array([ True, False, True, True], dtype=bool) >>> m = [1, 0, 1, 1] >>> ma.make_mask(m) array([ True, False, True, True], dtype=bool) >>> m = [1, 0, 2, -3] >>> ma.make_mask(m) array([ True, False, True, True], dtype=bool)
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], dtype=bool)
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.int, np.int]}) >>> arr = np.array(arr, dtype=dtype) >>> arr array([(1, 0), (0, 1), (1, 0), (1, 0)], dtype=[('man', '<i4'), ('mouse', '<i4')]) >>> ma.make_mask(arr, dtype=dtype) array([(True, False), (False, True), (True, False), (True, False)], dtype=[('man', '|b1'), ('mouse', '|b1')])
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https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.ma.make_mask.html