def test_calculate_mean_disp_for_single_ideal_mof():
""" Test trajectory class calculate_mean_disp function for single ideal mof """
traj = Trajectory(read=os.path.join(mof_trial_dir, 'Run1', 'traj.xyz'))
traj.set_cell([80, 80, 80])
traj.calculate_distances()
traj.calculate_mean_disp()
assert np.all(traj.mean_disp > 0.0)
assert np.all(traj.mean_disp < 0.6)
def test_calculate_mean_disp_and_mean_squared_disp_for_linear_motion_of_particles():
""" Test trajectory class calculate_mean_disp and calculate_mean_squared_disp functions
for 6 particles with linear motion with alternative calculation """
n_atoms, n_frames = 6, 6
traj = Trajectory()
traj.coordinates = [[[i + j, i + j, i + j] for i in range(n_atoms)] for j in range(n_frames)]
traj.n_atoms, traj.n_frames = n_atoms, n_frames
traj.set_cell([10, 10, 10])
traj.calculate_distances()
traj.calculate_mean_disp()
traj.calculate_mean_squared_disp()
assert np.allclose([np.sum(range(n_frames)) / n_frames * np.sqrt(3)] * n_atoms, traj.mean_disp)
for i in range(traj.n_atoms):
c0 = traj.coordinates[0][i]
x2 = (np.array([f[i][0] for f in traj.coordinates]) - c0[0]) ** 2
y2 = (np.array([f[i][1] for f in traj.coordinates]) - c0[1]) ** 2
z2 = (np.array([f[i][2] for f in traj.coordinates]) - c0[2]) ** 2
mean_squared_disp = sum(x2 + y2 + z2) / n_frames
mean_disp = sum(np.sqrt(x2 + y2 + z2)) / n_frames
assert np.allclose(mean_disp, traj.mean_disp[i])
assert np.allclose(mean_squared_disp, traj.mean_squared_disp[i])