def eigen_cov(x, y):
    mx = np.mean(x)
    my = np.mean(y)
    x = (x - mx)
    y = (y - my)
    cxx = np.var(x)
    cxy = 0
    cyy = np.var(y)
    nx = len(x)
    for ct in range(nx):
        cxy = (cxy + (x[ct] * y[ct]))
    cxy = (cxy / nx)
    C = np.zeros((2, 2))
    C[(0, 0)] = cxx
    C[(0, 1)] = cxy
    C[(1, 0)] = cxy
    C[(1, 1)] = cyy
    (D, V) = np.linalg.eig(C)
    return (V, D)
def eigen_cov(x, y):
    mx = np.mean(x)
    my = np.mean(y)
    x = (x - mx)
    y = (y - my)
    cxx = np.var(x)
    cxy = 0
    cyy = np.var(y)
    nx = len(x)
    for ct in range(nx):
        cxy = (cxy + (x[ct] * y[ct]))
    cxy = (cxy / nx)
    C = np.zeros((2, 2))
    C[(0, 0)] = cxx
    C[(0, 1)] = cxy
    C[(1, 0)] = cxy
    C[(1, 1)] = cyy
    (D, V) = np.linalg.eig(C)
    return (V, D)