numpy.ma.make_mask¶
-
numpy.ma.
make_mask
(m, copy=False, shrink=True, dtype=<class 'numpy.bool_'>)[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 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 casenomask
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':[int, 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')])