def crosscov_vector(x, y, nlags=None):
    N = x.shape[1]
    if nlags is None:
        nlags = N
    nc = x.shape[0]

    rxy = np.empty((nc, nc, nlags))

    # rxy(k) = E{ x(t)y*(t-k) } ( * = conj transpose )
    # Take the expectation over an outer-product
    # between x(t) and conj{y(t-k)} for each t

    for k in range(nlags):
        # rxy(k) = E{ x(t)y*(t-k) }
        prod = x[:, None, k:] * y[None, :, :N - k].conj()
##         # rxy(k) = E{ x(t)y*(t+k) }
##         prod = x[:,None,:N-k] * y[None,:,k:].conj()
        # Do a sample mean of N-k pts? or sum and divide by N?
        rxy[..., k] = prod.mean(axis=-1)
    return rxy
Is this L{INameable} known to the given C{observer} by the given
dataset/ETHPy150Open nipy/nitime/nitime/utils.py/crosscov_vector--crossover with--dataset/ETHPy150Open twisted/imaginary/imaginary/iimaginary.py/INameable.knownTo