oss one another:

    >>> np.convolve([1, 2, 3], [0, 1, 0.5])
    array([0. , 1. , 2.5, 4. , 1.5])

    Only return the middle values of the convolution.
    Contains boundary effects, where zeros are taken
    into account:

    >>> np.convolve([1,2,3],[0,1,0.5], 'same')
    array([1. ,  2.5,  4. ])

    The two arrays are of the same length, so there
    is only one position where they completely overlap:

    >>> np.convolve([1,2,3],[0,1,0.5], 'valid')
    array([2.5])

    Fr