Remove scaling tests.
@@ -9,10 +9,8 @@ import toppra
toppra.setup_logging(level="INFO")
-@pytest.mark.skip(reason="scaling is deprecated")
-@pytest.mark.parametrize("scaling", [1e-3, 1e-4])
@pytest.mark.parametrize("Ngrid", [101, 501, 1001])
-def test_scalar_zero_motion(scaling, Ngrid):
+def test_scalar_zero_motion(Ngrid):
"""The simple zero motion trajectory
Note: Paths with very small displacement, like the one given in
@@ -37,14 +35,13 @@ def test_scalar_zero_motion(scaling, Ngrid):
instance = toppra.algorithm.TOPPRA(
[pc_vel, pc_acc], path, solver_wrapper='hotqpoases',
- gridpoints=np.linspace(0, 1.0, Ngrid), scaling=scaling)
- jnt_traj, aux_traj, data = instance.compute_trajectory(0, 0, return_data=True)
+ gridpoints=np.linspace(0, 1.0, Ngrid), scaling=-1)
+ jnt_traj = instance.compute_trajectory(0, 0, return_data=True)
# Simply assert success
assert jnt_traj is not None
assert jnt_traj.duration < 9e-4 # less than 1ms
-@pytest.mark.skip(reason="scaling is deprecated")
@pytest.mark.parametrize("Ngrid", [101, 501, 1001])
def test_scalar_auto_scaling(Ngrid):
"""Automatic scaling should lead to better results at slower
@@ -66,7 +63,7 @@ def test_scalar_auto_scaling(Ngrid):
instance = toppra.algorithm.TOPPRA(
[pc_vel, pc_acc], path, solver_wrapper='hotqpoases',
gridpoints=np.linspace(0, 1.0, Ngrid), scaling=-1)
- jnt_traj, aux_traj, data = instance.compute_trajectory(0, 0, return_data=True)
+ jnt_traj = instance.compute_trajectory(0, 0, return_data=True)
# Simply assert success
assert jnt_traj is not None
