def test_penalty_custom_with_bounds_failing_max_bound(value):
def custom_with_bounds(ocp, nlp, t, x, u, p):
my_values = DM.zeros((12, 1)) + x[0]
return -10, my_values, 10
ocp = prepare_test_ocp()
x = [DM.ones((12, 1)) * value]
penalty_type = ConstraintFcn.CUSTOM
penalty = Constraint(penalty_type)
penalty.max_bound = 0
penalty.custom_function = custom_with_bounds
with pytest.raises(RuntimeError):
penalty_type.value[0](penalty, ocp, ocp.nlp[0], [], x, [], [])
def test_penalty_custom_with_bounds_failing_min_bound(value):
def custom_with_bounds(pn):
return -10, pn.nlp.states["q"].cx, 10
ocp = prepare_test_ocp()
t = [0]
x = [DM.ones((12, 1)) * value]
u = [0]
penalty_type = ConstraintFcn.CUSTOM
penalty = Constraint(penalty_type)
penalty.min_bound = 0
penalty.custom_function = custom_with_bounds
with pytest.raises(RuntimeError):
penalty_type.value[0](penalty,
PenaltyNodeList(ocp, ocp.nlp[0], t, x, [], []))
def test_penalty_custom_with_bounds(value):
def custom_with_bounds(ocp, nlp, t, x, u, p):
my_values = DM.zeros((12, 1)) + x[0]
return -10, my_values, 10
ocp = prepare_test_ocp()
x = [DM.ones((12, 1)) * value]
penalty_type = ConstraintFcn.CUSTOM
penalty = Constraint(penalty_type)
penalty.custom_function = custom_with_bounds
penalty_type.value[0](penalty, ocp, ocp.nlp[0], [], x, [], [])
res = ocp.nlp[0].g[0][0]["val"]
np.testing.assert_almost_equal(res, np.array([[value]] * 12))
np.testing.assert_almost_equal(ocp.nlp[0].g[0][0]["bounds"].min,
np.array([[-10]] * 12))
np.testing.assert_almost_equal(ocp.nlp[0].g[0][0]["bounds"].max,
np.array([[10]] * 12))
def test_penalty_custom_with_bounds_failing_max_bound(value):
def custom_with_bounds(pn):
return -10, pn.nlp.states["q"].cx, 10
ocp = prepare_test_ocp()
t = [0]
x = [DM.ones((12, 1)) * value]
u = [0]
penalty_type = ConstraintFcn.CUSTOM
penalty = Constraint(penalty_type)
penalty.max_bound = 0
penalty.custom_function = custom_with_bounds
with pytest.raises(
RuntimeError,
match=
"You cannot have non linear bounds for custom constraints and min_bound or max_bound defined",
):
penalty_type.value[0](penalty,
PenaltyNodeList(ocp, ocp.nlp[0], t, x, [], []))