def test_sigma_points_1D():
    """ tests passing 1D data into sigma_points"""

    kappa = 0.
    sp = JulierSigmaPoints(1, kappa)

    #ukf = UKF(dim_x=1, dim_z=1, dt=0.1, hx=None, fx=None, kappa=kappa)

    Wm, Wc = sp.weights()
    assert np.allclose(Wm, Wc, 1e-12)
    assert len(Wm) == 3

    mean = 5
    cov = 9

    Xi = sp.sigma_points (mean, cov)
    xm, ucov = unscented_transform(Xi,Wm, Wc, 0)

    # sum of weights*sigma points should be the original mean
    m = 0.0
    for x, w in zip(Xi, Wm):
        m += x*w

    assert abs(m-mean) >= 1.e-12
    assert abs(xm[0] - mean) < 1.e-12
    assert abs(ucov[0,0]-cov) < 1.e-12

    assert Xi.shape == (3,1)
dataset/ETHPy150Open rlabbe/filterpy/filterpy/kalman/tests/test_ukf.py/test_sigma_points_1D/operator `<` -> `>=`