-----
    The definition of correlation above is not unique and sometimes correlation
    may be defined differently. Another common definition is:

    .. math:: c'_k = \sum_n a_{n} \cdot \overline{v_{n+k}}

    which is related to :math:`c_k` by :math:`c'_k = c_{-k}`.

    `numpy.correlate` may perform slowly in large arrays (i.e. n = 1e5) because it does
    not use the FFT to compute the convolution; in that case, `scipy.signal.correlate` might
    be preferable.


    Examples
    --------
    >>> np.correlate([1, 2, 3], [0, 1, 0.5])
    array([3.5])
    >>> np.correlate([1, 2, 3], [0, 1, 0.5], "same")
    array([2. ,  3.5,  3. ])
    >>> np.correlate([1, 2, 3], [0, 1, 0.5], "full")
    array([0.5,  2. ,  3.5,  3. ,  0. ])

    Using complex sequences:

    >>> np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full')
    array([ 0.5-0.5j,  1.0+0.j ,  1.5-1.5j,  3.0-1.j ,  0.0+0.j ])

    Note that you get the time reversed, complex conjugated result
    (:math:`\overline{c_{-k}}`) when the two input sequences a and v change
    places:

    >>> np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full')
    array([ 0.0+0.j ,  3.0+1.j ,  1.5+1.5j,  1.0+0.j ,  0.5+0.5j])

    )