

def test_sigma_plot():
    " Test to make sure sigma's correctly mirror the shape and orientation\n    of the covariance array."
    x = np.array([[1, 2]])
    P = np.array([[2, 1.2], [1.2, 2]])
    kappa = 0.1
    sp0 = JulierSigmaPoints(n=2, kappa=kappa)
    sp1 = JulierSigmaPoints(n=2, kappa=(kappa * 1000))
    (w0, _) = sp0.weights()
    (w1, _) = sp1.weights()
    Xi0 = sp0.sigma_points(x, P)
    Xi1 = sp1.sigma_points(x, P)
    assert (max(Xi1[:, 0]) > max(Xi0[:, 0]))
    assert (max(Xi1[:, 1]) > max(Xi0[:, 1]))
    if DO_PLOT:
        plt.figure()
        for i in range(Xi0.shape[0]):
            plt.scatter((((Xi0[(i, 0)] - x[(0, 0)]) * w0[i]) + x[(0, 0)]), (((Xi0[(i, 1)] - x[(0, 1)]) * w0[i]) + x[(0, 1)]), color='blue')
        for i in range(Xi1.shape[0]):
            plt.scatter((((Xi1[(i, 0)] - x[(0, 0)]) * w1[i]) + x[(0, 0)]), (((Xi1[(i, 1)] - x[(0, 1)]) * w1[_]) + x[(0, 1)]), color='green')
        stats.plot_covariance_ellipse([1, 2], P)

dataset/ETHPy150Open rlabbe/filterpy/filterpy/kalman/tests/test_ukf.py test_sigma_plot/VarMisuse@366/445 `i`->`_`