def test_temporal_component_momentum(self):
# Random data for 3-vector
three_vec = np.array([3, .12, .45])
# Test lightlike four vector
v = np.zeros(4)
v[0] = geodesic_integrator.calculate_temporal_component(three_vec,
three_vec,
.4,
causality=0)
v[1:] = three_vec[:]
norm = np.dot(
np.dot(metric.inverse_metric(three_vec[0], three_vec[1], .4), v),
v)
npt.assert_almost_equal(norm, 0)
# Test timelike four vector
v = np.zeros(4)
v[0] = geodesic_integrator.calculate_temporal_component(three_vec,
three_vec,
a=.4,
causality=-1)
v[1:] = three_vec[:]
norm = np.dot(
np.dot(metric.inverse_metric(three_vec[0], three_vec[1], a=.4), v),
v)
npt.assert_almost_equal(norm, -1)
def test_temporal_component_momentum(self):
# Random data for 3-vector
three_vec = np.array([3,.12,.45])
# Test lightlike four vector
v = np.zeros(4)
v[0] = geodesic_integrator.calculate_temporal_component( three_vec,three_vec,.4,causality=0)
v[1:] = three_vec[:]
norm = np.dot(np.dot(metric.inverse_metric(three_vec[0],three_vec[1],.4),v),v)
npt.assert_almost_equal( norm, 0)
# Test timelike four vector
v = np.zeros(4)
v[0] = geodesic_integrator.calculate_temporal_component( three_vec,three_vec,a=.4,causality=-1)
v[1:] = three_vec[:]
norm = np.dot(np.dot(metric.inverse_metric(three_vec[0],three_vec[1],a=.4),v),v)
npt.assert_almost_equal( norm, -1)
def test_inverse(self):
kerr_metric = metric.metric(1.9, 0.2, 0.7)
kerr_inverse_metric = metric.inverse_metric(1.9, 0.2, 0.7)
numpy_inverse = np.linalg.inv(kerr_metric)
# Check that the calculated inverse is equal to numpy's inverse
npt.assert_almost_equal(kerr_inverse_metric, numpy_inverse)
def test_inverse(self):
kerr_metric = metric.metric(1.9,0.2,0.7)
kerr_inverse_metric = metric.inverse_metric(1.9,0.2,0.7)
numpy_inverse = np.linalg.inv(kerr_metric)
# Check that the calculated inverse is equal to numpy's inverse
npt.assert_almost_equal( kerr_inverse_metric, numpy_inverse)
