fixed parameters

bioptim/dynamics/configure_problem.py

@@ -1166,8 +1166,6 @@ def configure_dynamics_function(ocp, nlp, dyn_func, **extra_params):
             The function to get the derivative of the states
         """
 
-        DynamicsFunctions.apply_parameters(nlp)
-
         dynamics_eval = dyn_func(
             nlp.time_cx,
             nlp.states.scaled.cx,
@@ -1355,7 +1353,7 @@ def configure_soft_contact_function(ocp, nlp):
 
         q = nlp.states.cx[nlp.states["q"].index]
         qdot = nlp.states.cx[nlp.states["qdot"].index]
-        global_soft_contact_force_func = nlp.model.soft_contact_forces()(q, qdot)
+        global_soft_contact_force_func = nlp.model.soft_contact_forces()(q, qdot, nlp.parameters.cx)
         nlp.soft_contact_forces_func = global_soft_contact_force_func
 
         for i_sc in range(nlp.model.nb_soft_contacts):

bioptim/dynamics/dynamics_functions.py

@@ -147,8 +147,8 @@ def torque_driven(
         dq = DynamicsFunctions.compute_qdot(nlp, q, qdot)
 
         tau = DynamicsFunctions.__get_fatigable_tau(nlp, states, controls, fatigue)
-        tau = tau + nlp.model.passive_joint_torque()(q, qdot) if with_passive_torque else tau
-        tau = tau + nlp.model.ligament_joint_torque()(q, qdot) if with_ligament else tau
+        tau = tau + nlp.model.passive_joint_torque()(q, qdot, nlp.parameters.cx) if with_passive_torque else tau
+        tau = tau + nlp.model.ligament_joint_torque()(q, qdot, nlp.parameters.cx) if with_ligament else tau
         tau = tau - nlp.model.friction_coefficients @ qdot if with_friction else tau
 
         if (
@@ -260,7 +260,7 @@ def torque_driven_free_floating_base(
         n_qdot = nlp.model.nb_qdot
 
         tau_joints = (
-            tau_joints + nlp.model.passive_joint_torque()(q_full, qdot_full) if with_passive_torque else tau_joints
+            tau_joints + nlp.model.passive_joint_torque()(q_full, qdot_full, nlp.parameters.cx) if with_passive_torque else tau_joints
         )
         tau_joints = tau_joints + nlp.model.ligament_joint_torque()(q_full, qdot_full) if with_ligament else tau_joints
         tau_joints = tau_joints - nlp.model.friction_coefficients @ qdot_joints if with_friction else tau_joints
@@ -528,13 +528,13 @@ def torque_activations_driven(
         qdot = DynamicsFunctions.get(nlp.states["qdot"], states)
         tau_activation = DynamicsFunctions.get(nlp.controls["tau"], controls)
 
-        tau = nlp.model.torque()(tau_activation, q, qdot)
+        tau = nlp.model.torque()(tau_activation, q, qdot, nlp.parameters.cx)
         if with_passive_torque:
-            tau += nlp.model.passive_joint_torque()(q, qdot)
+            tau += nlp.model.passive_joint_torque()(q, qdot, nlp.parameters.cx)
         if with_residual_torque:
             tau += DynamicsFunctions.get(nlp.controls["residual_tau"], controls)
         if with_ligament:
-            tau += nlp.model.ligament_joint_torque()(q, qdot)
+            tau += nlp.model.ligament_joint_torque()(q, qdot, nlp.parameters.cx)
 
         dq = DynamicsFunctions.compute_qdot(nlp, q, qdot)
         ddq = DynamicsFunctions.forward_dynamics(nlp, q, qdot, tau, with_contact, external_forces)
@@ -598,8 +598,8 @@ def torque_derivative_driven(
         qdot = DynamicsFunctions.get(nlp.states["qdot"], states)
 
         tau = DynamicsFunctions.get(nlp.states["tau"], states)
-        tau = tau + nlp.model.passive_joint_torque()(q, qdot) if with_passive_torque else tau
-        tau = tau + nlp.model.ligament_joint_torque()(q, qdot) if with_ligament else tau
+        tau = tau + nlp.model.passive_joint_torque()(q, qdot, nlp.parameters.cx) if with_passive_torque else tau
+        tau = tau + nlp.model.ligament_joint_torque()(q, qdot, nlp.parameters.cx) if with_ligament else tau
 
         dq = DynamicsFunctions.compute_qdot(nlp, q, qdot)
         dtau = DynamicsFunctions.get(nlp.controls["taudot"], controls)
@@ -673,11 +673,11 @@ def forces_from_torque_driven(
         q = nlp.get_var_from_states_or_controls("q", states, controls)
         qdot = nlp.get_var_from_states_or_controls("qdot", states, controls)
         tau = nlp.get_var_from_states_or_controls("tau", states, controls)
-        tau = tau + nlp.model.passive_joint_torque()(q, qdot) if with_passive_torque else tau
-        tau = tau + nlp.model.ligament_joint_torque()(q, qdot) if with_ligament else tau
+        tau = tau + nlp.model.passive_joint_torque()(q, qdot, nlp.parameters.cx) if with_passive_torque else tau
+        tau = tau + nlp.model.ligament_joint_torque()(q, qdot, nlp.parameters.cx) if with_ligament else tau
 
         external_forces = [] if external_forces is None else external_forces
-        return nlp.model.contact_forces()(q, qdot, tau, external_forces)
+        return nlp.model.contact_forces()(q, qdot, tau, external_forces, nlp.parameters.cx)
 
     @staticmethod
     def forces_from_torque_activation_driven(
@@ -726,12 +726,12 @@ def forces_from_torque_activation_driven(
         q = nlp.get_var_from_states_or_controls("q", states, controls)
         qdot = nlp.get_var_from_states_or_controls("qdot", states, controls)
         tau_activations = nlp.get_var_from_states_or_controls("tau", states, controls)
-        tau = nlp.model.torque()(tau_activations, q, qdot)
-        tau = tau + nlp.model.passive_joint_torque()(q, qdot) if with_passive_torque else tau
-        tau = tau + nlp.model.ligament_joint_torque()(q, qdot) if with_ligament else tau
+        tau = nlp.model.torque()(tau_activations, q, qdot, nlp.parameters.cx)
+        tau = tau + nlp.model.passive_joint_torque()(q, qdot, nlp.parameters.cx) if with_passive_torque else tau
+        tau = tau + nlp.model.ligament_joint_torque()(q, qdot, nlp.parameters.cx) if with_ligament else tau
 
         external_forces = [] if external_forces is None else external_forces
-        return nlp.model.contact_forces()(q, qdot, tau, external_forces)
+        return nlp.model.contact_forces()(q, qdot, tau, external_forces, nlp.parameters.cx)
 
     @staticmethod
     def muscles_driven(
@@ -831,8 +831,8 @@ def muscles_driven(
         muscles_tau = DynamicsFunctions.compute_tau_from_muscle(nlp, q, qdot, mus_activations, fatigue_states)
 
         tau = muscles_tau + residual_tau if residual_tau is not None else muscles_tau
-        tau = tau + nlp.model.passive_joint_torque()(q, qdot) if with_passive_torque else tau
-        tau = tau + nlp.model.ligament_joint_torque()(q, qdot) if with_ligament else tau
+        tau = tau + nlp.model.passive_joint_torque()(q, qdot, nlp.parameters.cx) if with_passive_torque else tau
+        tau = tau + nlp.model.ligament_joint_torque()(q, qdot, nlp.parameters.cx) if with_ligament else tau
 
         dq = DynamicsFunctions.compute_qdot(nlp, q, qdot)
 
@@ -932,8 +932,8 @@ def forces_from_muscle_driven(
         muscles_tau = DynamicsFunctions.compute_tau_from_muscle(nlp, q, qdot, mus_activations)
 
         tau = muscles_tau + residual_tau if residual_tau is not None else muscles_tau
-        tau = tau + nlp.model.passive_joint_torque()(q, qdot) if with_passive_torque else tau
-        tau = tau + nlp.model.ligament_joint_torque()(q, qdot) if with_ligament else tau
+        tau = tau + nlp.model.passive_joint_torque()(q, qdot, nlp.parameters.cx) if with_passive_torque else tau
+        tau = tau + nlp.model.ligament_joint_torque()(q, qdot, nlp.parameters.cx) if with_ligament else tau
 
         external_forces = [] if external_forces is None else external_forces
         return nlp.model.contact_forces()(q, qdot, tau, external_forces)
@@ -1040,27 +1040,6 @@ def get(var: OptimizationVariable, cx: MX | SX):
 
         return var.mapping.to_second.map(cx[var.index, :])
 
-    @staticmethod
-    def apply_parameters(nlp):
-        """
-        Apply the parameter variables to the model. This should be called before calling the dynamics
-
-        Parameters
-        ----------
-        parameters: MX.sym | SX.sym
-            The state of the system
-        nlp: NonLinearProgram
-            The definition of the system
-        """
-
-        for param_key in nlp.parameters:
-            # Call the pre dynamics function
-            if nlp.parameters[param_key].function:
-                param = nlp.parameters[param_key]
-                param_scaling = nlp.parameters[param_key].scaling.scaling
-                param_reduced = nlp.parameters.scaled.cx[param.index]
-                param.function(nlp.model, param_reduced * param_scaling, **param.kwargs)
-
     @staticmethod
     def compute_qdot(nlp, q: MX | SX, qdot: MX | SX):
         """
@@ -1093,7 +1072,7 @@ def compute_qdot(nlp, q: MX | SX, qdot: MX | SX):
             mapping = nlp.controls["q"].mapping
         else:
             raise RuntimeError("Your q key combination was not found in states or controls")
-        return mapping.to_first.map(nlp.model.reshape_qdot()(q, qdot))
+        return mapping.to_first.map(nlp.model.reshape_qdot()(q, qdot, nlp.parameters.cx))
 
     @staticmethod
     def forward_dynamics(
@@ -1134,10 +1113,10 @@ def forward_dynamics(
             qdot_var_mapping = BiMapping([i for i in range(qdot.shape[0])], [i for i in range(qdot.shape[0])]).to_first
 
         if external_forces is None:
-            qddot = nlp.model.forward_dynamics(with_contact=with_contact)(q, qdot, tau, [])
+            qddot = nlp.model.forward_dynamics(with_contact=with_contact)(q, qdot, tau, [], nlp.parameters.cx)
             return qdot_var_mapping.map(qddot)
         else:
-            qddot = nlp.model.forward_dynamics(with_contact=with_contact)(q, qdot, tau, external_forces)
+            qddot = nlp.model.forward_dynamics(with_contact=with_contact)(q, qdot, tau, external_forces, nlp.parameters.cx)
             return qdot_var_mapping.map(qddot)
 
     @staticmethod
@@ -1173,7 +1152,7 @@ def inverse_dynamics(
         """
 
         if external_forces is None:
-            tau = nlp.model.inverse_dynamics(with_contact=with_contact)(q, qdot, qddot, [])
+            tau = nlp.model.inverse_dynamics(with_contact=with_contact)(q, qdot, qddot, [], nlp.parameters.cx)
         else:
             if "tau" in nlp.states:
                 tau_shape = nlp.states["tau"].cx.shape[0]
@@ -1183,7 +1162,7 @@ def inverse_dynamics(
                 tau_shape = nlp.model.nb_tau
             tau = nlp.cx(tau_shape, nlp.ns)
             for i in range(external_forces.shape[1]):
-                tau[:, i] = nlp.model.inverse_dynamics(with_contact=with_contact)(q, qdot, qddot, external_forces[:, i])
+                tau[:, i] = nlp.model.inverse_dynamics(with_contact=with_contact)(q, qdot, qddot, external_forces[:, i], nlp.parameters.cx)
         return tau  # We ignore on purpose the mapping to keep zeros in the defects of the dynamic.
 
     @staticmethod
@@ -1240,7 +1219,7 @@ def compute_tau_from_muscle(
                 activations = vertcat(activations, muscle_activations[k] * (1 - fatigue_states[k]))
             else:
                 activations = vertcat(activations, muscle_activations[k])
-        return nlp.model.muscle_joint_torque()(activations, q, qdot)
+        return nlp.model.muscle_joint_torque()(activations, q, qdot, nlp.parameters.cx)
 
     @staticmethod
     def holonomic_torque_driven(
@@ -1283,6 +1262,6 @@ def holonomic_torque_driven(
         q_u = DynamicsFunctions.get(nlp.states["q_u"], states)
         qdot_u = DynamicsFunctions.get(nlp.states["qdot_u"], states)
         tau = DynamicsFunctions.get(nlp.controls["tau"], controls)
-        qddot_u = nlp.model.partitioned_forward_dynamics(q_u, qdot_u, tau, external_forces=external_forces)
+        qddot_u = nlp.model.partitioned_forward_dynamics(q_u, qdot_u, tau, external_forces, nlp.parameters.cx)
 
         return DynamicsEvaluation(dxdt=vertcat(qdot_u, qddot_u), defects=None)

bioptim/examples/getting_started/custom_objectives.py

@@ -108,7 +108,6 @@ def prepare_ocp(
         first_marker="m0",
         second_marker="m1",
         weight=1000,
-        method=0,
     )
     objective_functions.add(
         custom_func_track_markers,
@@ -118,7 +117,6 @@ def prepare_ocp(
         first_marker="m0",
         second_marker="m2",
         weight=1000,
-        method=1,
     )
 
     # Dynamics

bioptim/examples/getting_started/custom_parameters.py

@@ -49,7 +49,7 @@ def my_parameter_function(bio_model: BiorbdModel, value: MX, extra_value: Any):
     """
 
     value[2] *= extra_value
-    bio_model.set_gravity(value)
+    bio_model.model.setGravity(value)
 
 
 def set_mass(bio_model: BiorbdModel, value: MX):
@@ -65,7 +65,7 @@ def set_mass(bio_model: BiorbdModel, value: MX):
         The CasADi variables to modify the model
     """
 
-    bio_model.segments[0].characteristics().setMass(value)
+    bio_model.model.segments()[0].characteristics().setMass(value)
 
 
 def my_target_function(controller: PenaltyController, key: str) -> MX:
@@ -149,32 +149,6 @@ def prepare_ocp(
     The ocp ready to be solved
     """
 
-    # --- Options --- #
-    bio_model = BiorbdModel(biorbd_model_path)
-    n_tau = bio_model.nb_tau
-
-    # Add objective functions
-    objective_functions = ObjectiveList()
-    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1)
-    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", weight=1)
-
-    # Dynamics
-    dynamics = Dynamics(DynamicsFcn.TORQUE_DRIVEN, expand_dynamics=expand_dynamics, phase_dynamics=phase_dynamics)
-
-    # Path constraint
-    x_bounds = BoundsList()
-    x_bounds["q"] = bio_model.bounds_from_ranges("q")
-    x_bounds["q"][:, [0, -1]] = 0
-    x_bounds["q"][1, -1] = 3.14
-    x_bounds["qdot"] = bio_model.bounds_from_ranges("qdot")
-    x_bounds["qdot"][:, [0, -1]] = 0
-
-    # Define control path constraint
-    tau_min, tau_max = -300, 300
-    u_bounds = BoundsList()
-    u_bounds["tau"] = [tau_min] * n_tau, [tau_max] * n_tau
-    u_bounds["tau"][1, :] = 0
-
     # Define the parameter to optimize
     parameters = ParameterList(use_sx=use_sx)
     parameter_objectives = ParameterObjectiveList()
@@ -230,6 +204,32 @@ def prepare_ocp(
             key="mass",
         )
 
+    # --- Options --- #
+    bio_model = BiorbdModel(biorbd_model_path, parameters=parameters)
+    n_tau = bio_model.nb_tau
+
+    # Add objective functions
+    objective_functions = ObjectiveList()
+    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1)
+    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", weight=1)
+
+    # Dynamics
+    dynamics = Dynamics(DynamicsFcn.TORQUE_DRIVEN, expand_dynamics=expand_dynamics, phase_dynamics=phase_dynamics)
+
+    # Path constraint
+    x_bounds = BoundsList()
+    x_bounds["q"] = bio_model.bounds_from_ranges("q")
+    x_bounds["q"][:, [0, -1]] = 0
+    x_bounds["q"][1, -1] = 3.14
+    x_bounds["qdot"] = bio_model.bounds_from_ranges("qdot")
+    x_bounds["qdot"][:, [0, -1]] = 0
+
+    # Define control path constraint
+    tau_min, tau_max = -300, 300
+    u_bounds = BoundsList()
+    u_bounds["tau"] = [tau_min] * n_tau, [tau_max] * n_tau
+    u_bounds["tau"][1, :] = 0
+
     return OptimalControlProgram(
         bio_model,
         dynamics,

bioptim/examples/moving_horizon_estimation/mhe.py

@@ -42,20 +42,15 @@
 def states_to_markers(bio_model, states):
     nq = bio_model.nb_q
     n_mark = bio_model.nb_markers
-    q = cas.MX.sym("q", nq)
-    markers_func = biorbd.to_casadi_func("makers", bio_model.markers, q)
-    return np.array(markers_func(states[:nq, :])).reshape((3, n_mark, -1), order="F")
+    return np.array(bio_model.markers()(states[:nq, :])).reshape((3, n_mark, -1), order="F")
 
 
 def generate_data(bio_model, tf, x0, t_max, n_shoot, noise_std, show_plots=False):
     def pendulum_ode(t, x, u):
-        return np.concatenate((x[nq:, np.newaxis], qddot_func(x[:nq], x[nq:], u)))[:, 0]
+        return np.concatenate((x[nq:, np.newaxis], qddot_func(x[:nq], x[nq:], u, [])))[:, 0]
 
     nq = bio_model.nb_q
-    q = cas.MX.sym("q", nq)
-    qdot = cas.MX.sym("qdot", nq)
-    tau = cas.MX.sym("tau", nq)
-    qddot_func = biorbd.to_casadi_func("forw_dyn", bio_model.forward_dynamics, q, qdot, tau)
+    qddot_func = bio_model.forward_dynamics()
 
     # Simulated data
     dt = tf / n_shoot

bioptim/examples/track/track_segment_on_rt.py

@@ -73,7 +73,7 @@ def prepare_ocp(
 
     # Constraints
     constraints = ConstraintList()
-    constraints.add(ConstraintFcn.TRACK_SEGMENT_WITH_CUSTOM_RT, node=Node.ALL, segment="seg_rt", rt=0)
+    constraints.add(ConstraintFcn.TRACK_SEGMENT_WITH_CUSTOM_RT, node=Node.ALL, segment="seg_rt", rt_idx=0)
 
     # Path constraint
     x_bounds = BoundsList()