 integer
            with n bits.  In that case, peak-to-peak values greater than
            ``2**(n-1)-1`` will be returned as negative values. An example
            with a work-around is shown below.

        Parameters
        ----------
        axis : {None, int}, optional
            Axis along which to find the peaks.  If None (default) the
            flattened array is used.
        out : {None, array_like}, optional
            Alternative output array in which to place the result. It must
            have the same shape and buffer length as the expected output
            but the type will be cast if necessary.
        fill_value : scalar or None, optional
            Value used to fill in the masked values.
        keepdims : bool, optional
            If this is set to True, the axes which are reduced are left
            in the result as dimensions with size one. With this option,
            the result will broadcast correctly against the array.

        Returns
        -------
        ptp : ndarray.
            A new array holding the result, unless ``out`` was
            specified, in which case a reference to ``out`` is returned.

        Examples
        --------
        >>> x = np.ma.MaskedArray([[4, 9, 2, 10],
        ...                        [6, 9, 7, 12]])

        >>> x.ptp(axis=1)
        masked_array(data=[8, 6],
                     mask=False,
               fill_value=999999)

        >>> x.ptp(axis=0)
        masked_array(data=[2, 0, 5, 2],
                     mask=False,
               fill_value=999999)

        >>> x.ptp()
        10

        This example shows that a negative value can be returned when
        the input is an array of signed integers.

        >>> y = np.ma.MaskedArray([[1, 127],
        ...                        [0, 127],
        ...                        [-1, 127],
        ...                        [-2, 127]], dtype=np.int8)
        >>> y.ptp(axis=1)
        masked_array(data=[ 126,  127, -128, -127],
                     mask=False,
               fill_value=999999,
                    dtype=int8)

        A work-around is to use the `view()` method to view the result as
        unsigned integers with the same bit width:

        >>> y.ptp(axis=1).view(np.uint8)
        masked_array(data=[126, 127, 128, 129],
                     mask=False,
               fill_value=999999,
                    dtype=uint8)
        N)