Function make_mask

numpy/ma/core.py:1555–1641

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 interpreted as F

(m, copy=False, shrink=True, dtype=MaskType)

def make_mask(m, copy=False, shrink=True, dtype=MaskType):
    """
    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 interpreted 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))