def test_get_filtered_val_not_filtered(ft_ll_mvar_1d, Yt_mvar_diffuse_missing):
"""
Test error message when fit is not run
"""
kf = Filter(ft_ll_mvar_1d, Yt_mvar_diffuse_missing, for_smoother=True)
with pytest.raises(TypeError) as error:
kf.get_filtered_val()
expected_result = 'The Kalman filter object is not fitted yet'
result = str(error.value)
assert result == expected_result
def test_get_filtered_val_missing(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing,
theta_ll_mvar_diffuse):
"""
Test missing measurements handling
"""
kf = Filter(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing, for_smoother=True)
kf.fit(theta_ll_mvar_diffuse)
Yt_filtered, Yt_filtered_cov, _, _ = kf.get_filtered_val()
np.testing.assert_array_equal(kf.Ht[2].dot(kf.xi_t[2][0]), Yt_filtered[2])
def test_get_filtered_y_no_xi(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing,
theta_ll_mvar_diffuse):
"""
Test df without xi
"""
kf = Filter(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing, for_smoother=True)
kf.fit(theta_ll_mvar_diffuse)
Yt_filtered, Yt_filtered_cov, xi_t, P_t = kf.get_filtered_val(is_xi=False)
assert np.isnan(xi_t[-1])
assert np.isnan(P_t[-1])
def test_get_filtered_val_all_xi(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing,
theta_ll_mvar_diffuse):
"""
Test df with all xi
"""
kf = Filter(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing, for_smoother=True)
kf.fit(theta_ll_mvar_diffuse)
Yt_filtered, Yt_filtered_cov, xi_t, P_t = kf.get_filtered_val()
np.testing.assert_array_equal(xi_t[2], kf.xi_t[2][0])
np.testing.assert_array_equal(P_t[2], np.nan * np.ones(P_t[2].shape))
np.testing.assert_array_equal(P_t[3], kf.P_star_t[3][0])
