multi_model

bioptim/examples/getting_started/custom_dynamics.py

@@ -74,7 +74,7 @@ def custom_dynamics(
 
     # You can directly call biorbd function (as for ddq) or call bioptim accessor (as for dq)
     dq = DynamicsFunctions.compute_qdot(nlp, q, qdot) * my_additional_factor
-    ddq = nlp.model.forward_dynamics(q, qdot, tau, [])
+    ddq = nlp.model.forward_dynamics()(q, qdot, tau, [])
 
     # the user has to choose if want to return the explicit dynamics dx/dt = f(x,u,p)
     # as the first argument of DynamicsEvaluation or

bioptim/examples/muscle_driven_ocp/muscle_excitations_tracker.py

@@ -12,7 +12,7 @@
 
 import biorbd_casadi as biorbd
 import numpy as np
-from casadi import MX, vertcat
+from casadi import MX, SX, vertcat, horzcat
 from matplotlib import pyplot as plt
 from scipy.integrate import solve_ivp
 
@@ -44,6 +44,7 @@ def generate_data(
     n_shooting: int,
     use_residual_torque: bool = True,
     phase_dynamics: PhaseDynamics = PhaseDynamics.SHARED_DURING_THE_PHASE,
+    use_sx: bool = False,
 ) -> tuple:
     """
     Generate random data. If np.random.seed is defined before, it will always return the same results
@@ -91,7 +92,8 @@ def generate_data(
     symbolic_controls = vertcat(*(symbolic_tau, symbolic_mus_controls))
 
     symbolic_parameters = MX.sym("u", 0, 0)
-    nlp = NonLinearProgram(phase_dynamics=phase_dynamics)
+    nlp = NonLinearProgram(phase_dynamics=phase_dynamics, use_sx=use_sx)
+    nlp.cx = SX if use_sx else MX
     nlp.model = bio_model
     nlp.variable_mappings = {
         "q": BiMapping(range(n_q), range(n_q)),
@@ -100,8 +102,6 @@ def generate_data(
         "tau": BiMapping(range(n_tau), range(n_tau)),
         "muscles": BiMapping(range(n_mus), range(n_mus)),
     }
-    markers_func = biorbd.to_casadi_func("ForwardKin", bio_model.markers, symbolic_q)
-
     nlp.states = OptimizationVariableContainer(phase_dynamics=phase_dynamics)
     nlp.states_dot = OptimizationVariableContainer(phase_dynamics=phase_dynamics)
     nlp.controls = OptimizationVariableContainer(phase_dynamics=phase_dynamics)
@@ -202,7 +202,7 @@ def dyn_interface(t, x, u):
 
     def add_to_data(i, q):
         X[:, i] = q
-        markers[:, :, i] = markers_func(q[:n_q])
+        markers[:, :, i] = horzcat(*bio_model.markers()(q[:n_q]))
 
     x_init = np.array([0.0] * n_q + [0.0] * n_qdot + [0.5] * n_mus)
     add_to_data(0, x_init)
@@ -374,10 +374,8 @@ def main():
     n_frames = q.shape[1]
 
     markers = np.ndarray((3, n_mark, q.shape[1]))
-    symbolic_states = MX.sym("x", n_q, 1)
-    markers_func = biorbd.to_casadi_func("ForwardKin", bio_model.markers, symbolic_states)
     for i in range(n_frames):
-        markers[:, :, i] = markers_func(q[:, i])
+        markers[:, :, i] = horzcat(*bio_model.markers()(q[:, i]))
 
     plt.figure("Markers")
     n_steps_ode = ocp.nlp[0].ode_solver.steps + 1 if ocp.nlp[0].ode_solver.is_direct_collocation else 1

bioptim/limits/constraints.py

@@ -414,7 +414,8 @@ def qddot_equals_forward_dynamics(
             tau = tau + controller.model.ligament_joint_torque(q, qdot) if with_ligament else tau
 
             qddot = controller.controls["qddot"].cx if "qddot" in controller.controls else controller.states["qddot"].cx
-            qddot_fd = controller.model.forward_dynamics(with_contact=with_contact)(q, qdot, tau)
+            # @Ipuch: no f_ext allowed ?
+            qddot_fd = controller.model.forward_dynamics(with_contact=with_contact)(q, qdot, tau, [])
             return qddot - qddot_fd
 
         @staticmethod

bioptim/models/biorbd/biorbd_model.py

@@ -221,7 +221,7 @@ def center_of_mass_acceleration(self) -> Function:
         biorbd_return = self.model.CoMddot(q_biorbd, qdot_biorbd, qddot_biorbd, True).to_mx()
         casadi_fun = Function(
             "center_of_mass_acceleration",
-            [self.q, self.qdot, self.tau],
+            [self.q, self.qdot, self.qddot],
             [biorbd_return],
         )
         return casadi_fun