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