def test_calculate_trajectory(
pos_vec, vel_vec, time, M, a, start_lambda, end_lambda, OdeMethodKwargs
):
_scr = schwarzschild_radius(M).value
obj = Kerr.from_BL(pos_vec, vel_vec, time, M, a)
ans = obj.calculate_trajectory(
start_lambda=start_lambda,
end_lambda=end_lambda,
OdeMethodKwargs=OdeMethodKwargs,
)
ans = ans[1]
testarray = list()
for ansi in ans:
g = kerr_utils.metric(_c, ansi[1], ansi[2], _scr, a)
tmp = (
g[0][0] * (ansi[4] ** 2)
+ g[1][1] * (ansi[5] ** 2)
+ g[2][2] * (ansi[6] ** 2)
+ g[3][3] * (ansi[7] ** 2)
+ 2 * g[0][3] * ansi[4] * ansi[7]
)
testarray.append(tmp)
testarray = np.array(testarray, dtype=float)
comparearray = np.ones(shape=ans[:, 4].shape, dtype=float)
assert_allclose(testarray, comparearray, 1e-4)
def test_compare_kerr_and_schwarzschild_metric():
r = 99.9
theta = 5 * np.pi / 6
Rs = 9e-3
# Kerr metric would reduce to Schwarzschild metric under limits a=0
mk = kerr_utils.metric(_c, r, theta, Rs, 0.0)
ms = schwarzschild_utils.metric(_c, r, theta, Rs)
assert_allclose(mk, ms, rtol=1e-8)
def test_compare_kerr_and_schwarzschild_metric():
r = 99.9
theta = 5 * np.pi / 6
M = 6.73317655e26
# Kerr metric would reduce to Schwarzschild metric under limits a=0
mk = kerr_utils.metric(r, theta, M, 0.0)
ms = schwarzschild_utils.metric(r, theta, M)
assert_allclose(mk, ms, rtol=1e-8)
def test_calculate_trajectory(coords, time, M, start_lambda, end_lambda,
OdeMethodKwargs):
_scr = schwarzschild_radius_dimensionless(M)
obj = Kerr.from_BL(coords, M, time)
ans = obj.calculate_trajectory(
start_lambda=start_lambda,
end_lambda=end_lambda,
OdeMethodKwargs=OdeMethodKwargs,
)
ans = ans[1]
testarray = list()
for i in ans:
g = kerr_utils.metric(i[1], i[2], M.value, coords.a.to(u.m).value)
testarray.append(g[0][0] * (i[4]**2) + g[1][1] * (i[5]**2) + g[2][2] *
(i[6]**2) + g[3][3] * (i[7]**2) +
2 * g[0][3] * i[4] * i[7])
testarray = np.array(testarray, dtype=float)
comparearray = np.ones(shape=ans[:, 4].shape, dtype=float)
assert_allclose(testarray, comparearray, 1e-4)