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test_penalty.py
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test_penalty.py
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import pytest
from casadi import DM, MX, vertcat, horzcat
import numpy as np
from bioptim import (
BiorbdModel,
OptimalControlProgram,
DynamicsList,
DynamicsFcn,
Objective,
ObjectiveFcn,
Axis,
ConstraintFcn,
Constraint,
MultinodeConstraintFcn,
MultinodeConstraintList,
MultinodeConstraint,
MultinodeObjective,
Node,
RigidBodyDynamics,
ControlType,
PhaseDynamics,
ConstraintList,
)
from bioptim.limits.penalty_controller import PenaltyController
from bioptim.limits.penalty import PenaltyOption
from bioptim.misc.mapping import BiMapping
from bioptim.optimization.non_linear_program import NonLinearProgram as NLP
from bioptim.optimization.optimization_variable import OptimizationVariableList
from tests.utils import TestUtils
def prepare_test_ocp(
phase_dynamics,
with_muscles=False,
with_contact=False,
with_actuator=False,
implicit=False,
use_sx=True,
):
bioptim_folder = TestUtils.bioptim_folder()
if with_muscles and with_contact or with_muscles and with_actuator or with_contact and with_actuator:
raise RuntimeError("With muscles and with contact and with_actuator together is not defined")
if with_muscles and implicit or implicit and with_actuator:
raise RuntimeError("With muscles and implicit and with_actuator together is not defined")
elif with_muscles:
bio_model = BiorbdModel(bioptim_folder + "/examples/muscle_driven_ocp/models/arm26.bioMod")
dynamics = DynamicsList()
dynamics.add(
DynamicsFcn.MUSCLE_DRIVEN, with_residual_torque=True, expand_dynamics=True, phase_dynamics=phase_dynamics
)
elif with_contact:
bio_model = BiorbdModel(
bioptim_folder + "/examples/muscle_driven_with_contact/models/2segments_4dof_2contacts_1muscle.bioMod"
)
dynamics = DynamicsList()
rigidbody_dynamics = RigidBodyDynamics.DAE_INVERSE_DYNAMICS if implicit else RigidBodyDynamics.ODE
dynamics.add(
DynamicsFcn.TORQUE_DRIVEN,
with_contact=True,
expand_dynamics=True,
phase_dynamics=phase_dynamics,
rigidbody_dynamics=rigidbody_dynamics,
)
elif with_actuator:
bio_model = BiorbdModel(bioptim_folder + "/examples/torque_driven_ocp/models/cube.bioMod")
dynamics = DynamicsList()
dynamics.add(DynamicsFcn.TORQUE_DRIVEN, expand_dynamics=True, phase_dynamics=phase_dynamics)
else:
bio_model = BiorbdModel(bioptim_folder + "/examples/track/models/cube_and_line.bioMod")
dynamics = DynamicsList()
dynamics.add(DynamicsFcn.TORQUE_DRIVEN, expand_dynamics=True, phase_dynamics=phase_dynamics)
objective_functions = Objective(ObjectiveFcn.Mayer.MINIMIZE_TIME)
ocp = OptimalControlProgram(
bio_model,
dynamics,
10,
1.0,
objective_functions=objective_functions,
use_sx=use_sx,
)
ocp.nlp[0].J = [[]]
ocp.nlp[0].g = [[]]
return ocp
def get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d):
if isinstance(penalty, MultinodeConstraint) or isinstance(penalty, MultinodeObjective):
controller = [
PenaltyController(ocp, ocp.nlp[0], t, x, u, [], [], p, a, [], d, 0) for i in range(len(penalty.nodes_phase))
]
else:
controller = PenaltyController(ocp, ocp.nlp[0], t, x, u, [], [], p, a, [], d, 0)
val = penalty.type(penalty, controller, **penalty.extra_parameters)
# changed only this one
if isinstance(val, float):
return val
time = ocp.nlp[0].time_cx
phases_dt_cx = vertcat(*[nlp.dt for nlp in ocp.nlp])
states = ocp.nlp[0].states.cx_start if ocp.nlp[0].states.cx_start.shape != (0, 0) else ocp.cx(0, 0)
controls = ocp.nlp[0].controls.cx_start if ocp.nlp[0].controls.cx_start.shape != (0, 0) else ocp.cx(0, 0)
parameters = ocp.nlp[0].parameters.cx if ocp.nlp[0].parameters.cx.shape != (0, 0) else ocp.cx(0, 0)
algebraic_states = (
ocp.nlp[0].algebraic_states.cx_start if ocp.nlp[0].algebraic_states.cx_start.shape != (0, 0) else ocp.cx(0, 0)
)
dynamics_constants = (
ocp.nlp[0].dynamics_constants.cx if ocp.nlp[0].dynamics_constants.cx.shape != (0, 0) else ocp.cx(0, 0)
)
return ocp.nlp[0].to_casadi_func(
"penalty", val, time, phases_dt_cx, states, controls, parameters, algebraic_states, dynamics_constants
)(t, phases_dt, x[0], u[0], p, a, d)
def test_penalty_targets_shapes():
p = ObjectiveFcn.Parameter
np.testing.assert_equal(Objective([], custom_type=p, target=1).target.shape, (1, 1))
np.testing.assert_equal(Objective([], custom_type=p, target=np.array(1)).target.shape, (1, 1))
np.testing.assert_equal(Objective([], custom_type=p, target=[1]).target.shape, (1, 1))
np.testing.assert_equal(Objective([], custom_type=p, target=[1, 2]).target.shape, (2, 1))
np.testing.assert_equal(Objective([], custom_type=p, target=[[1], [2]]).target.shape, (2, 1))
np.testing.assert_equal(Objective([], custom_type=p, target=[[1, 2]]).target.shape, (1, 2))
np.testing.assert_equal(Objective([], custom_type=p, target=np.array([[1, 2]])).target.shape, (1, 2))
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_time(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0.05 * ocp.nlp[0].ns]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = [1]
a = []
d = []
penalty_type = penalty_origin.MINIMIZE_TIME
penalty = Objective(penalty_type)
penalty_type(penalty, PenaltyController(ocp, ocp.nlp[0], [], [], [], [], [], p, a, [], d, 0))
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
if penalty_origin == ObjectiveFcn.Lagrange:
np.testing.assert_almost_equal(res, np.array(1))
else:
np.testing.assert_almost_equal(res, np.array(0.05) * ocp.nlp[0].ns)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_state(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty = Objective(penalty_origin.MINIMIZE_STATE, key="qdot")
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
np.testing.assert_almost_equal(res, np.array([[value]] * 4))
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_joint_power(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [1]
p = []
a = []
d = []
penalty = Objective(penalty_origin.MINIMIZE_POWER, key_control="tau")
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
np.testing.assert_almost_equal(res, np.array([[value]] * 4))
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_muscle_power(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(with_muscles=True, phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [1]
u = [DM.ones((8, 1)) * value]
p = []
a = []
d = []
penalty = Objective(penalty_origin.MINIMIZE_POWER, key_control="muscles")
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
if value == 0.1:
np.testing.assert_almost_equal(
res, np.array([[0.00475812, -0.00505504, -0.000717714, 0.00215864, 0.00215864, -0.00159915]]).T
)
else:
np.testing.assert_almost_equal(
res, np.array([[-0.475812, 0.505504, 0.0717714, -0.215864, -0.215864, 0.159915]]).T, decimal=5
)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_qddot(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [1]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value, DM.ones((8, 1)) * value]
u = [DM.ones((4, 1)) * value]
p = []
a = []
d = []
if penalty_origin == ConstraintFcn:
with pytest.raises(AttributeError, match="MINIMIZE_QDDOT"):
_ = penalty_origin.MINIMIZE_QDDOT
return
else:
penalty_type = penalty_origin.MINIMIZE_QDDOT
penalty = Objective(penalty_type)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d).T
np.testing.assert_almost_equal(res, [[value, -9.81 + value, value, value]])
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_state(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.TRACK_STATE
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, key="qdot", target=np.ones((4, 1)) * value)
else:
penalty = Constraint(penalty_type, key="qdot", target=np.ones((4, 1)) * value)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
np.testing.assert_almost_equal(res, [[value]] * 4)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_joint_power(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [1]
p = []
a = []
d = []
penalty_type = penalty_origin.TRACK_POWER
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, key_control="tau")
else:
penalty = Constraint(penalty_type, key_control="tau")
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
np.testing.assert_almost_equal(res, [[value]] * 4)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_markers(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.MINIMIZE_MARKERS
penalty = Objective(penalty_type)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array(
[
[0.1, 0.99517075, 1.9901749, 1.0950042, 0, 1, 2, 0.49750208],
[0, 0, 0, 0, 0, 0, 0, 0],
[0.1, -0.9948376, -1.094671, 0.000166583, 0, 0, 0, -0.0499167],
]
)
if value == -10:
expected = np.array(
[
[-10, -11.3830926, -12.2221642, -10.8390715, 0, 1.0, 2.0, -0.4195358],
[0, 0, 0, 0, 0, 0, 0, 0],
[-10, -9.7049496, -10.2489707, -10.5440211, 0, 0, 0, -0.2720106],
]
)
np.testing.assert_almost_equal(res, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_markers(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.TRACK_MARKERS
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, target=np.ones((3, 7, 1)) * value)
else:
penalty = Constraint(penalty_type, target=np.ones((3, 7, 1)) * value)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array(
[
[0.1, 0.99517075, 1.9901749, 1.0950042, 0, 1, 2, 0.49750208],
[0, 0, 0, 0, 0, 0, 0, 0],
[0.1, -0.9948376, -1.094671, 0.000166583, 0, 0, 0, -0.0499167],
]
)
if value == -10:
expected = np.array(
[
[-10, -11.3830926, -12.2221642, -10.8390715, 0, 1.0, 2.0, -0.4195358],
[0, 0, 0, 0, 0, 0, 0, 0],
[-10, -9.7049496, -10.2489707, -10.5440211, 0, 0, 0, -0.2720106],
]
)
np.testing.assert_almost_equal(res, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_markers_velocity(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.MINIMIZE_MARKERS_VELOCITY
penalty = Objective(penalty_type)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
if value == 0.1:
np.testing.assert_almost_equal(
res,
np.array(
[
[0.1, -0.00948376, -0.0194671, 0.0900167, 0, 00, 00, -0.00499167],
[0, 0, 0, 0, 0, 00, 00, 0],
[0.1, 0.0104829, -0.0890175, 0.000499583, 0, 0, 0, -0.0497502],
]
),
)
else:
np.testing.assert_almost_equal(
res,
np.array(
[
[-10, -12.9505, -7.51029, -4.55979, 0, 00, 00, 2.72011],
[0, 0, 0, 0, 0, 00, 00, 0],
[-10, -23.8309, -32.2216, -18.3907, 0, 0, 0, -4.19536],
]
),
decimal=4,
)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
@pytest.mark.parametrize("implicit", [True, False])
def test_penalty_minimize_markers_acceleration(penalty_origin, implicit, value, phase_dynamics):
ocp = prepare_test_ocp(implicit=implicit, phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = [0]
a = []
d = []
penalty_type = penalty_origin.MINIMIZE_MARKERS_ACCELERATION
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type)
else:
penalty = Constraint(penalty_type)
if not implicit:
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array(
[
[2.15106e-16, -0.00895171, -0.0189017, -0.00995004, 00, 00, 00, -0.00497502],
[0, 0, 0, 0, 00, 00, 00, 0],
[-9.81, -9.79905, -9.79805, -9.809, 00, 00, 00, 0.000499167],
]
)
if value == -10:
expected = np.array(
[
[0.0, 138.309264, 222.2164169, 83.90715291, 0.0, 0.0, 0.0, 41.95357645],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-9.81, -39.31504182, 15.08706927, 44.59211109, 0.0, 0.0, 0.0, 27.20105554],
]
)
np.testing.assert_almost_equal(res, expected, decimal=5)
else:
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array(
[
[2.15105711e-16, -8.95170749e-03, -1.89017491e-02, -9.95004165e-03, 00, 00, 00, -4.97502083e-03],
[0, 0, 0, 0, 00, 00, 00, 0],
[-9.81, -9.79905162e00, -9.79805329e00, -9.80900167e00, 00, 00, 00, 4.99167083e-04],
]
)
if value == -10:
expected = np.array(
[
[0.0, 138.309264, 222.2164169, 83.90715291, 0.0, 0.0, 0.0, 41.95357645],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-9.81, -39.31504182, 15.08706927, 44.59211109, 0.0, 0.0, 0.0, 27.20105554],
]
)
np.testing.assert_almost_equal(
res,
expected,
decimal=5,
)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_markers_velocity(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.TRACK_MARKERS_VELOCITY
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, target=np.ones((3, 7, 1)) * value)
else:
penalty = Constraint(penalty_type, target=np.ones((3, 7, 1)) * value)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
if value == 0.1:
np.testing.assert_almost_equal(
res,
np.array(
[
[0.1, -0.00948376, -0.0194671, 0.0900167, 0, 00, 00, -0.00499167],
[0, 0, 0, 0, 0, 00, 00, 0],
[0.1, 0.0104829, -0.0890175, 0.000499583, 0, 0, 0, -0.0497502],
]
),
)
else:
np.testing.assert_almost_equal(
res,
np.array(
[
[-10, -12.9505, -7.51029, -4.55979, 0, 00, 00, 2.72011],
[0, 0, 0, 0, 0, 00, 00, 0],
[-10, -23.8309, -32.2216, -18.3907, 0, 0, 0, -4.19536],
]
),
decimal=4,
)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
@pytest.mark.parametrize("implicit", [True, False])
def test_penalty_track_markers_acceleration(penalty_origin, value, implicit, phase_dynamics):
ocp = prepare_test_ocp(implicit=implicit, phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
penalty_type = penalty_origin.TRACK_MARKERS_ACCELERATION
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, target=np.ones((3, 7, 1)) * value)
else:
penalty = Constraint(penalty_type, target=np.ones((3, 7, 1)) * value)
if not implicit:
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, [], [], [])
expected = np.array(
[
[2.15106e-16, -0.00895171, -0.0189017, -0.00995004, 00, 00, 00, -0.00497502],
[0, 0, 0, 0, 00, 00, 00, 0],
[-9.81, -9.79905, -9.79805, -9.809, 00, 00, 00, 0.000499167],
]
)
if value == -10:
expected = np.array(
[
[0.0, 138.309264, 222.2164169, 83.90715291, 0.0, 0.0, 0.0, 41.95357645],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-9.81, -39.31504182, 15.08706927, 44.59211109, 0.0, 0.0, 0.0, 27.20105554],
]
)
np.testing.assert_almost_equal(res, expected, decimal=5)
else:
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, [], [], [])
expected = np.array(
[
[2.15105711e-16, -8.95170749e-03, -1.89017491e-02, -9.95004165e-03, 00, 00, 00, -4.97502083e-03],
[0, 0, 0, 0, 00, 00, 00, 0],
[-9.81, -9.79905162e00, -9.79805329e00, -9.80900167e00, 00, 00, 00, 4.99167083e-04],
]
)
if value == -10:
expected = np.array(
[
[0.0, 138.309264, 222.2164169, 83.90715291, 0.0, 0.0, 0.0, 41.95357645],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-9.81, -39.31504182, 15.08706927, 44.59211109, 0.0, 0.0, 0.0, 27.20105554],
]
)
np.testing.assert_almost_equal(
res,
expected,
decimal=5,
)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_super_impose_marker(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.SUPERIMPOSE_MARKERS
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, first_marker=0, second_marker=1)
else:
penalty = Constraint(penalty_type, first_marker=0, second_marker=1)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = [[0.8951707, 0, -1.0948376]] if value == 0.1 else [[-1.3830926, 0, 0.2950504]]
np.testing.assert_almost_equal(res.T, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_super_impose_marker_velocity(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.SUPERIMPOSE_MARKERS_VELOCITY
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, first_marker=0, second_marker=1)
else:
penalty = Constraint(penalty_type, first_marker=0, second_marker=1)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = [[-0.1094838, 0.0, -0.0895171]] if value == 0.1 else [[-2.9505042, 0.0, -13.8309264]]
np.testing.assert_almost_equal(res.T, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
@pytest.mark.parametrize("value_intercept", [0.0, 1.0])
def test_penalty_proportional_state(penalty_origin, value, value_intercept, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.PROPORTIONAL_STATE
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(
penalty_type,
key="qdot",
first_dof=0,
second_dof=1,
coef=2,
first_dof_intercept=value_intercept,
second_dof_intercept=value_intercept,
)
else:
penalty = Constraint(
penalty_type,
key="qdot",
first_dof=0,
second_dof=1,
coef=2,
first_dof_intercept=value_intercept,
second_dof_intercept=value_intercept,
)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
if value_intercept == 0.0:
np.testing.assert_almost_equal(res, -value)
else:
if value == 0.1:
np.testing.assert_almost_equal(res, 0.9)
else:
np.testing.assert_almost_equal(res, 11)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_proportional_control(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [0]
u = [DM.ones((4, 1)) * value]
p = []
a = []
d = []
penalty_type = penalty_origin.PROPORTIONAL_CONTROL
first = 0
second = 1
coef = 2
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, key="tau", first_dof=first, second_dof=second, coef=coef)
else:
penalty = Constraint(penalty_type, key="tau", first_dof=first, second_dof=second, coef=coef)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
np.testing.assert_almost_equal(res, np.array(u[0][first] - coef * u[0][second]))
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_torque(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [0]
u = [DM.ones((4, 1)) * value]
p = []
a = []
d = []
penalty = Objective(penalty_origin.MINIMIZE_CONTROL, key="tau")
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
np.testing.assert_almost_equal(res, np.array([[value, value, value, value]]).T)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_torque(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [0]
u = [DM.ones((4, 1)) * value]
p = []
a = []
d = []
penalty_type = penalty_origin.TRACK_CONTROL
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, key="tau", target=np.ones((4, 1)) * value)
else:
penalty = Constraint(penalty_type, key="tau", target=np.ones((4, 1)) * value)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
np.testing.assert_almost_equal(res, np.array([[value, value, value, value]]).T)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_muscles_control(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(with_muscles=True, phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [0]
u = [DM.ones((8, 1)) * value]
p = []
a = []
d = []
penalty_type = penalty_origin.MINIMIZE_CONTROL
penalty = Objective(penalty_type, key="muscles")
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
np.testing.assert_almost_equal(res, np.array([[value, value, value, value, value, value]]).T)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_contact_forces(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(with_contact=True, phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [DM.ones((4, 1)) * value]
p = []
a = []
d = []
penalty_type = penalty_origin.MINIMIZE_CONTACT_FORCES
penalty = Objective(penalty_type)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
if value == 0.1:
np.testing.assert_almost_equal(res, np.array([[-9.6680105, 127.2360329, 5.0905995]]).T)
else:
np.testing.assert_almost_equal(res, np.array([[25.6627161, 462.7973306, -94.0182191]]).T)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_contact_forces(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(with_contact=True, phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [DM.ones((4, 1)) * value]
p = []
a = []
d = []
penalty_type = penalty_origin.TRACK_CONTACT_FORCES
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, target=np.ones((1, 1)) * value, index=0)
else:
penalty = Constraint(penalty_type, target=np.ones((1, 1)) * value, index=0)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
if value == 0.1:
np.testing.assert_almost_equal(res.T, [[-9.6680105, 127.2360329, 5.0905995]])
else:
np.testing.assert_almost_equal(res.T, [[25.6627161, 462.7973306, -94.0182191]])
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_predicted_com_height(value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = ObjectiveFcn.Mayer.MINIMIZE_PREDICTED_COM_HEIGHT
penalty = Objective(penalty_type)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array(0.0501274 if value == 0.1 else -3.72579)
np.testing.assert_almost_equal(res, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_com_position(value, penalty_origin, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
if "TRACK_COM_POSITION" in penalty_origin._member_names_:
penalty_type = penalty_origin.TRACK_COM_POSITION
else:
penalty_type = penalty_origin.MINIMIZE_COM_POSITION
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type)
else:
penalty = Constraint(penalty_type)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array([[0.05], [0.05], [0.05]])
if value == -10:
expected = np.array([[-5], [0.05], [-5]])
np.testing.assert_almost_equal(res, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_minimize_angular_momentum(value, penalty_origin, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.MINIMIZE_ANGULAR_MOMENTUM
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type)
else:
penalty = Constraint(penalty_type)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array([[-0.005], [0.2], [0.005]])
if value == -10:
expected = np.array([[0.5], [-20], [-0.5]])
np.testing.assert_almost_equal(res, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
@pytest.mark.parametrize("use_sx", [True, False])
def test_penalty_minimize_linear_momentum(value, penalty_origin, use_sx, phase_dynamics):
ocp = prepare_test_ocp(use_sx=use_sx, phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.MINIMIZE_LINEAR_MOMENTUM
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type)
else:
penalty = Constraint(penalty_type)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array([[0.1], [0], [0.1]])
if value == -10:
expected = np.array([[-10], [0], [-10]])
np.testing.assert_almost_equal(res, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer])
@pytest.mark.parametrize("value", [0.1, -10])
@pytest.mark.parametrize("implicit", [True, False])
def test_penalty_minimize_comddot(value, penalty_origin, implicit, phase_dynamics):
ocp = prepare_test_ocp(with_contact=True, implicit=implicit, phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.MINIMIZE_COM_ACCELERATION
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type)
else:
penalty = Constraint(penalty_type)
if not implicit:
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array([[0.0], [-0.7168803], [-0.0740871]])
if value == -10:
expected = np.array([[0.0], [1.455063], [16.3741091]])
np.testing.assert_almost_equal(res, expected)
else:
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, [], [], [])
expected = np.array([[0], [-0.0008324], [0.002668]])
if value == -10:
expected = np.array([[0], [-17.5050533], [-18.2891901]])
np.testing.assert_almost_equal(res, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_segment_with_custom_rt(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []
penalty_type = penalty_origin.TRACK_SEGMENT_WITH_CUSTOM_RT
if isinstance(penalty_type, (ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer)):
penalty = Objective(penalty_type, segment="ground", rt=0)
else:
penalty = Constraint(penalty_type, segment="ground", rt=0)
res = get_penalty_value(ocp, penalty, t, phases_dt, x, u, p, a, d)
expected = np.array([[0], [0.1], [0]])
if value == -10:
expected = np.array([[3.1415927], [0.575222], [3.1415927]])
np.testing.assert_almost_equal(res, expected)
@pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])
@pytest.mark.parametrize("penalty_origin", [ObjectiveFcn.Lagrange, ObjectiveFcn.Mayer, ConstraintFcn])
@pytest.mark.parametrize("value", [0.1, -10])
def test_penalty_track_marker_with_segment_axis(penalty_origin, value, phase_dynamics):
ocp = prepare_test_ocp(phase_dynamics=phase_dynamics)
t = [0]
phases_dt = [0.05]
x = [DM.ones((8, 1)) * value]
u = [0]
p = []
a = []
d = []