d.

    Parameters
    ----------
    condition : array_like, bool
        Where True, yield `x`, otherwise yield `y`.
    x, y : array_like
        Values from which to choose. `x`, `y` and `condition` need to be
        broadcastable to some shape.

    Returns
    -------
    out : ndarray
        An array with elements from `x` where `condition` is True, and elements
        from `y` elsewhere.

    See Also
    --------
    choose
    nonzero : The function that is called when x and y are omitted

    Notes
    -----
    If all the arrays are 1-D, `where` is equivalent to::

        [xv if c else yv
         for c, xv, yv in zip(condition, x, y)]

    Examples
    --------
    >>> a = np.arange(10)
    >>> a
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    >>> np.where(a < 5, a, 10*a)
    array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

    This can be used on multidimensional arrays too:

    >>> np.where([[True, False], [True, True]],
    ...          [[1, 2], [3, 4]],
    ...          [[9, 8], [7, 6]])
    array([[1, 8],
           [3, 4]])

    The shapes of x, y, and the condition are broadcast together:

    >>> x, y = np.ogrid[:3, :4]
    >>> np.where(x < y, x, 10 + y)  # both x and 10+y are broadcast
    array([[10,  0,  0,  0],
           [10, 11,  1,  1],
           [10, 11, 12,  2]])

    >>> a = np.array([[0, 1, 2],
    ...               [0, 2, 4],
    ...               [0, 3, 6]])
    >>> np.where(a < 4, a, -1)  # -1 is broadcast
    array([[ 0,  1,  2],
           [ 0,  2, -1],
           [ 0,  3, -1]])
    ro