def diff(x,order=1,period=None, _cache=_cache):
    tmp = asarray(x)
    if order == 0:
        return tmp
    if iscomplexobj(tmp):
        return diff(tmp.real,order,period)+1j*diff(tmp.imag,order,period)
    if period is not None:
        c = 2*pi/period
    else:
        c = 1.0
    n = len(x)
    omega = _cache.get((n,order,c))
    if omega is None:
        if len(_cache) > 20:
            while _cache:
                _cache.popitem()

        def kernel(k,order=order,c=c):
            if k:
                return pow(c*k,order)
            return 0
        omega = convolve.init_convolution_kernel(n,kernel,d=order,
                                                 zero_nyquist=1)
        _cache[(n,order,c)] = omega
    overwrite_x = _datacopied(tmp, x)
    return convolve.convolve(tmp,omega,swap_real_imag=order % 2,
                             overwrite_x=overwrite_x)
Given a code tuple, reconstruct an approximate vector.
dataset/ETHPy150Open scipy/scipy/scipy/fftpack/pseudo_diffs.py/diff--crossover with--dataset/ETHPy150Open yahoo/lopq/python/lopq/model.py/LOPQModel.reconstruct