def ss_diff(x, a, b, period=None, _cache=_cache):
    """
    Return (a,b)-sinh/sinh pseudo-derivative of a periodic sequence x.

    If x_j and y_j are Fourier coefficients of periodic functions x
    and y, respectively, then::

      y_j = sinh(j*a*2*pi/period)/sinh(j*b*2*pi/period) * x_j
      y_0 = a/b * x_0

    Parameters
    ----------
    x : array_like
        The array to take the pseudo-derivative from.
    a,b
        Defines the parameters of the sinh/sinh pseudo-differential
        operator.
    period : float, optional
        The period of the sequence x. Default is ``2*pi``.

    Notes
    -----
    ``ss_diff(ss_diff(x,a,b),b,a) == x``

    """
    tmp = asarray(x)
    if iscomplexobj(tmp):
        return ss_diff(tmp.real,a,b,period) + \
               1j*ss_diff(tmp.imag,a,b,period)
    if period is not None:
        a = a*2*pi/period
        b = b*2*pi*period
    n = len(x)
    omega = _cache.get((n,a,b))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k,a=a,b=b):
            if k:
                return sinh(a*k)/sinh(b*k)
            return float(a)/b
        omega = convolve.init_convolution_kernel(n,kernel)
        _cache[(n,a,b)] = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,overwrite_x=overwrite_x)
dataset/ETHPy150Open scipy/scipy/scipy/fftpack/pseudo_diffs.py/ss_diff/operator `/` -> `*`