def _crosscov(x, y, axis=-1, all_lags=False, debias=True):
    """Returns the crosscovariance sequence between two ndarrays.
    This is performed by calling fftconvolve on x, y[::-1]

    Parameters


    x: ndarray
    y: ndarray
    axis: time axis

    all_lags: {True/False}
    whether to return all nonzero lags, or to clip the length of s_xy
    to be the length of x and y. If False, then the zero lag covariance
    is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2

    debias: {True/False}
    Always removes an estimate of the mean along the axis, unless
    told not to.


    cross covariance is defined as
    sxy[k] := E{X[t]*Y[t+k]}, where X,Y are zero mean random processes
    """
    if x.shape[axis] != y.shape[axis]:
        raise ValueError(
            'crosscov() only works on same-length sequences for now'
            )
    if debias:
        x = _remove_bias(x, axis)
        y = _remove_bias(y, axis)
    slicing = [slice(d) for d in x.shape]
    slicing[axis] = slice(None,None,-1)
    sxy = _fftconvolve(x, y[tuple(slicing)], axis=axis, mode='full')
    N = x.shape[axis]
    sxy /= N
    if all_lags:
        return sxy
    slicing[axis] = slice(N-1,2*N-1)
    return sxy[tuple(slicing)]
def _crosscov(x, y, axis=-1, all_lags=False, debias=True):
    """Returns the crosscovariance sequence between two ndarrays.
    This is performed by calling fftconvolve on x, y[::-1]

    Parameters


    x: ndarray
    y: ndarray
    axis: time axis

    all_lags: {True/False}
    whether to return all nonzero lags, or to clip the length of s_xy
    to be the length of x and y. If False, then the zero lag covariance
    is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2

    debias: {True/False}
    Always removes an estimate of the mean along the axis, unless
    told not to.


    cross covariance is defined as
    sxy[k] := E{X[t]*Y[t+k]}, where X,Y are zero mean random processes
    """
    if x.shape[axis] != y.shape[axis]:
        raise ValueError(
            'crosscov() only works on same-length sequences for now'
            )
    if debias:
        x = _remove_bias(x, axis)
        y = _remove_bias(y, axis)
    slicing = [slice(d) for d in x.shape]
    slicing[axis] = slice(None,None,-1)
    sxy = _fftconvolve(x, y[tuple(slicing)], axis=axis, mode='full')
    N = x.shape[axis]
    sxy /= N
    if all_lags:
        return sxy
    slicing[axis] = slice(N-1,2*N-1)
    return sxy[tuple(slicing)]
Returns the crosscovariance sequence between two ndarrays.
    This is performed by calling fftconvolve on x, y[::-1]

    Parameters


    x: ndarray
    y: ndarray
    axis: time axis

    all_lags: {True/False}
    whether to return all nonzero lags, or to clip the length of s_xy
    to be the length of x and y. If False, then the zero lag covariance
    is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2

    debias: {True/False}
    Always removes an estimate of the mean along the axis, unless
    told not to.


    cross covariance is defined as
    sxy[k] := E{X[t]*Y[t+k]}, where X,Y are zero mean random processes