def test_eval_trajectory(self):
line = Trajectory.fit(np.array([[0.3, 0.3, 0.4], [0, 0, 1]]))
# test integer
self.assertTrue(np.all(np.isclose(line.eval_y(0), np.array([0, 0, 1]))))
# test float
self.assertTrue(np.all(np.isclose(line.eval_y(0.0), np.array([0, 0, 1]))))
# test list
self.assertTrue(np.all(np.isclose(line.eval_y([0.0, 0.0]), np.array([0, 0, 1]))))
# test array
arr = np.array([0.0, 0.0])[..., np.newaxis]
self.assertTrue(np.all(np.isclose(line.eval_y(arr), np.array([0, 0, 1]))))
# test void direction
vertical = Trajectory.fit(np.array([[0, 0, 0], [0, 0, 1]]))
self.assertTrue(np.all(np.isnan(vertical.eval_x(5))))
def test_project_mindist(self):
# test min dist
traj = Trajectory.fit(np.array([[0.3, 0.3, 0.4], [0, 0, 1]]))
min_dist = np.sqrt(np.sum(traj.project(np.array([0, 0, 0])) ** 2))
assert np.isclose(min_dist, traj.mindist(np.array([0, 0, 0])))
# test projection, single point and vectorized
point = np.array([0.06656238, 0.47127062, 0.17440139])
expected = [0.36483837, 0.36483837, 0.27032326]
assert np.all(np.isclose(traj.project(point), expected))
assert np.all(np.isclose(traj.project(np.tile(point, (2, 1))), np.tile(expected, (2, 1))))
def test_exit_volume(self):
bc = BrainCoordinates((11, 13, 15), xyz0=(-5, -6, -7))
# test arbitrary line
line = Trajectory.fit(np.array([[0.1, 0.1, 0], [0, 0, 1]]))
epoints = Trajectory.exit_points(line, bc)
self.assertTrue(np.all(np.isclose(epoints, np.array([[0.8, 0.8, -7.], [-0.6, -0.6, 7.]]))))
# test apline
hline = Trajectory.fit(np.array([[0, 0, 0], [0, 1, 0]]))
epoints = Trajectory.exit_points(hline, bc)
self.assertTrue(np.all(np.isclose(epoints, np.array([[0, -6, 0], [0, 6, 0]]))))
# test mlline
hline = Trajectory.fit(np.array([[0, 0, 0], [1, 0, 0]]))
epoints = Trajectory.exit_points(hline, bc)
self.assertTrue(np.all(np.isclose(epoints, np.array([[-5, 0, 0], [5, 0, 0]]))))
# test vertical line
vline = Trajectory.fit(np.array([[0, 0, 0], [0, 0, 1]]))
epoints = Trajectory.exit_points(vline, bc)
self.assertTrue(np.all(np.isclose(epoints, np.array([[0, 0, -7.], [0, 0, 7.]]))))
def test_trajectory(self):
np.random.seed(42)
xyz = np.zeros([120, 3])
xyz[:, 0] = np.linspace(1, 9, 120)
xyz[:, 1] = np.linspace(2, 4, 120)
xyz[:, 2] = np.linspace(-2, 3, 120)
xyz += np.random.normal(size=xyz.shape) * 0.4
traj = Trajectory.fit(xyz)
# import matplotlib.pyplot as plt
# import mpl_toolkits.mplot3d as m3d
# ax = m3d.Axes3D(plt.figure())
# ax.scatter3D(*xyz.T)
# ax.plot3D(*insertion.eval_x(np.array([0, 10])).T)
# ax.plot3D(*insertion.eval_y(xyz[:, 1]).T, 'r')
d = xyz[:, 0] - traj.eval_y(xyz[:, 1])[:, 0]
self.assertTrue(np.abs(np.mean(d)) < 0.001)
d = xyz[:, 0] - traj.eval_y(xyz[:, 1])[:, 0]
self.assertTrue(np.abs(np.mean(d)) < 0.001)
d = xyz[:, 1] - traj.eval_z(xyz[:, 2])[:, 1]
self.assertTrue(np.abs(np.mean(d)) < 0.001)
