update and scoot some examples

bioptim/examples/muscle_driven_with_contact/contact_forces_inequality_constraint_muscle.py

@@ -32,7 +32,7 @@ def prepare_ocp(biorbd_model_path, phase_time, n_shooting, min_bound, max_bound)
     tau_min, tau_max, tau_init = -500.0, 500.0, 0.0
     activation_min, activation_max, activation_init = 0.0, 1.0, 0.5
     dof_mapping = BiMappingList()
-    dof_mapping.add("tau", [None, None, None, 0], [3])
+    dof_mapping.add("tau", bimapping=None, to_second=[None, None, None, 0], to_first=[3])
 
     # Add objective functions
     objective_functions = ObjectiveList()
@@ -66,34 +66,36 @@ def prepare_ocp(biorbd_model_path, phase_time, n_shooting, min_bound, max_bound)
 
     # Initialize x_bounds
     x_bounds = BoundsList()
-    x_bounds.add(bounds=bio_model.bounds_from_ranges(["q", "qdot"]))
-    x_bounds[0][:, 0] = pose_at_first_node + [0] * n_qdot
+    x_bounds["q"] = bio_model.bounds_from_ranges("q")
+    x_bounds["q"][:, 0] = pose_at_first_node
+    x_bounds["qdot"] = bio_model.bounds_from_ranges("qdot")
+    x_bounds["qdot"][:, 0] = 0
 
     # Initial guess
     x_init = InitialGuessList()
-    x_init.add(pose_at_first_node + [0] * n_qdot)
+    x_init["q"] = pose_at_first_node
+    x_init["qdot"] = np.zeros((n_qdot,))
 
     # Define control path constraint
     u_bounds = BoundsList()
-    u_bounds.add(
-        [tau_min] * len(dof_mapping["tau"].to_first) + [activation_min] * bio_model.nb_muscles,
-        [tau_max] * len(dof_mapping["tau"].to_first) + [activation_max] * bio_model.nb_muscles,
-    )
+    u_bounds["tau"] = [tau_min] * len(dof_mapping["tau"].to_first), [tau_max] * len(dof_mapping["tau"].to_first)
+    u_bounds["muscles"] = [activation_min] * bio_model.nb_muscles, [activation_max] * bio_model.nb_muscles
 
     u_init = InitialGuessList()
-    u_init.add([tau_init] * len(dof_mapping["tau"].to_first) + [activation_init] * bio_model.nb_muscles)
+    u_init["tau"] = [tau_init] * len(dof_mapping["tau"].to_first)
+    u_init["muscles"] = [activation_init] * bio_model.nb_muscles
     # ------------- #
 
     return OptimalControlProgram(
         bio_model,
         dynamics,
         n_shooting,
         phase_time,
-        x_init,
-        u_init,
-        x_bounds,
-        u_bounds,
-        objective_functions,
+        x_bounds=x_bounds,
+        u_bounds=u_bounds,
+        x_init=x_init,
+        u_init=u_init,
+        objective_functions=objective_functions,
         constraints=constraints,
         variable_mappings=dof_mapping,
         assume_phase_dynamics=True,

bioptim/examples/muscle_driven_with_contact/contact_forces_inequality_constraint_muscle_excitations.py

@@ -22,7 +22,6 @@
     SelectionMapping,
     Dependency,
     BoundsList,
-    Bounds,
     InitialGuessList,
     OdeSolver,
     Solver,
@@ -38,12 +37,9 @@ def prepare_ocp(biorbd_model_path, phase_time, n_shooting, min_bound, ode_solver
     # adds a bimapping to bimappinglist
     # dof_mapping.add("tau", [None, None, None, 0], [3])
     # easier way is to use SelectionMapping which is a subclass of biMapping
-    bimap = SelectionMapping(
-        nb_elements=bio_model.nb_dof,
-        independent_indices=(3,),
-        dependencies=(Dependency(dependent_index=None, reference_index=None, factor=None),),
-    )
-    dof_mapping.add(name="tau", bimapping=bimap)
+    dof_mapping = BiMappingList()
+    dof_mapping.add("tau", bimapping=None, to_second=[None, None, None, 0], to_first=[3])
+
     # Add objective functions
     objective_functions = ObjectiveList()
     objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_PREDICTED_COM_HEIGHT, weight=-1)
@@ -77,34 +73,38 @@ def prepare_ocp(biorbd_model_path, phase_time, n_shooting, min_bound, ode_solver
 
     # Initialize x_bounds
     x_bounds = BoundsList()
-    x_bounds.add(bounds=bio_model.bounds_from_ranges(["q", "qdot"]))
-    x_bounds[0].concatenate(Bounds([activation_min] * n_mus, [activation_max] * n_mus))
-    x_bounds[0][:, 0] = pose_at_first_node + [0] * n_qdot + [0.5] * n_mus
+    x_bounds["q"] = bio_model.bounds_from_ranges("q")
+    x_bounds["q"][:, 0] = pose_at_first_node
+    x_bounds["qdot"] = bio_model.bounds_from_ranges("qdot")
+    x_bounds["qdot"][:, 0] = np.zeros((n_qdot,))
+    x_bounds["muscles"] = [[activation_min] * n_mus, [activation_max] * n_mus]
+    x_bounds["muscles"][:, 0] = np.zeros((n_mus,))
 
     # Initial guess
     x_init = InitialGuessList()
-    x_init.add(pose_at_first_node + [0] * n_qdot + [0.5] * n_mus)
+    x_init["q"] = pose_at_first_node
+    x_init["qdot"] = np.zeros((n_qdot,))
+    x_init["muscles"] = np.zeros((n_mus,))
 
     # Define control path constraint
     u_bounds = BoundsList()
-    u_bounds.add(
-        [torque_min] * len(dof_mapping["tau"].to_first) + [activation_min] * bio_model.nb_muscles,
-        [torque_max] * len(dof_mapping["tau"].to_first) + [activation_max] * bio_model.nb_muscles,
-    )
+    u_bounds["tau"] = [torque_min] * len(dof_mapping["tau"].to_first), [torque_max] * len(dof_mapping["tau"].to_first)
+    u_bounds["muscles"] = [activation_min] * bio_model.nb_muscles, [activation_max] * bio_model.nb_muscles
 
     u_init = InitialGuessList()
-    u_init.add([torque_init] * len(dof_mapping["tau"].to_first) + [activation_init] * bio_model.nb_muscles)
+    u_init["tau"] = [torque_init] * len(dof_mapping["tau"].to_first)
+    u_init["muscles"] = [activation_init] * bio_model.nb_muscles
     # ------------- #
 
     return OptimalControlProgram(
         bio_model,
         dynamics,
         n_shooting,
         phase_time,
-        x_init,
-        u_init,
-        x_bounds,
-        u_bounds,
+        x_bounds=x_bounds,
+        u_bounds=u_bounds,
+        x_init=x_init,
+        u_init=u_init,
         objective_functions=objective_functions,
         constraints=constraints,
         variable_mappings=dof_mapping,

bioptim/optimization/solution.py

@@ -25,6 +25,7 @@
 from ..optimization.optimization_vector import OptimizationVectorHelper
 from ..dynamics.ode_solver import OdeSolver
 from ..interfaces.solve_ivp_interface import solve_ivp_interface, solve_ivp_bioptim_interface
+from ..interfaces.biorbd_model import BiorbdModel
 
 
 class Solution:
@@ -1401,12 +1402,8 @@ def animate(
             tracked_markers = [None for _ in range(len(self.ocp.nlp))]
 
         # detect this is the same instance over each phase
-
-        first_phase_model_type = type(self.nlp[0].model)
-        if not all([type(nlp.model) == first_phase_model_type for nlp in self.nlp]):
-            raise RuntimeError(
-                "The animation is only available for the same model type that imported your model."
-            )
+        if not all([isinstance(nlp.model, BiorbdModel) for nlp in self.ocp.nlp]):
+            raise RuntimeError("The animation is only available for the same model type that imported your model.")
 
         # assuming that all the models or the same type.
         self.ocp.nlp[0].model.animate(

tests/test_all_examples.py

@@ -230,10 +230,10 @@ def test__getting_started__example_multinode_objective():
     )
 
     with pytest.raises(
-        ValueError,
-        match="Valid values for setting the cx is 0, 1 or 2. If you reach this error message, you probably tried to "
-        "add more penalties than available in a multinode constraint. You can try to split the constraints "
-        "into more penalties or use assume_phase_dynamics=False.",
+            ValueError,
+            match="Valid values for setting the cx is 0, 1 or 2. If you reach this error message, you probably tried to "
+                  "add more penalties than available in a multinode constraint. You can try to split the constraints "
+                  "into more penalties or use assume_phase_dynamics=False.",
     ):
         ocp_module.prepare_ocp(
             biorbd_model_path=bioptim_folder + "/models/pendulum.bioMod",
@@ -255,7 +255,7 @@ def test__getting_started__example_save_and_load(assume_phase_dynamics):
 
     if not assume_phase_dynamics:
         with pytest.raises(
-            RuntimeError, match="n_threads is greater than 1 is not compatible with assume_phase_dynamics=False"
+                RuntimeError, match="n_threads is greater than 1 is not compatible with assume_phase_dynamics=False"
         ):
             ocp_module.prepare_ocp(
                 biorbd_model_path=bioptim_folder + "/models/pendulum.bioMod",
@@ -610,3 +610,61 @@ def test__inverse_optimal_control__double_pendulum_torque_driven_IOCP(assume_pha
         coefficients=[1, 1, 1],
         biorbd_model_path=bioptim_folder + "/models/double_pendulum.bioMod",
     )
+
+
+@pytest.mark.parametrize("assume_phase_dynamics", [False])
+def test__contact_and_muscle_forces_example(assume_phase_dynamics):
+    from bioptim.examples.muscle_driven_with_contact import contact_forces_inequality_constraint_muscle as ocp_module
+
+    bioptim_folder = os.path.dirname(ocp_module.__file__)
+
+    ocp_module.prepare_ocp(
+        biorbd_model_path=bioptim_folder + "/models/2segments_4dof_2contacts_1muscle.bioMod",
+        phase_time=0.3,
+        n_shooting=10,
+        min_bound=50,
+        max_bound=np.inf,
+    )
+
+
+@pytest.mark.parametrize("assume_phase_dynamics", [False])
+def test__contact_and_muscle_forces_example(assume_phase_dynamics):
+    from bioptim.examples.muscle_driven_with_contact import contact_forces_inequality_constraint_muscle as ocp_module
+
+    bioptim_folder = os.path.dirname(ocp_module.__file__)
+
+    ocp_module.prepare_ocp(
+        biorbd_model_path=bioptim_folder + "/models/2segments_4dof_2contacts_1muscle.bioMod",
+        phase_time=0.3,
+        n_shooting=10,
+        min_bound=50,
+        max_bound=np.inf,
+    )
+
+
+def test__contact_and_muscle_forces_example():
+    from bioptim.examples.muscle_driven_with_contact import contact_forces_inequality_constraint_muscle as ocp_module
+
+    bioptim_folder = os.path.dirname(ocp_module.__file__)
+
+    ocp_module.prepare_ocp(
+        biorbd_model_path=bioptim_folder + "/models/2segments_4dof_2contacts_1muscle.bioMod",
+        phase_time=0.3,
+        n_shooting=10,
+        min_bound=50,
+        max_bound=np.inf,
+    )
+
+
+def test__contact_and_muscle_forces_example_excitation():
+    from bioptim.examples.muscle_driven_with_contact import contact_forces_inequality_constraint_muscle_excitations as ocp_module
+
+    bioptim_folder = os.path.dirname(ocp_module.__file__)
+
+    ocp_module.prepare_ocp(
+        biorbd_model_path=bioptim_folder + "/models/2segments_4dof_2contacts_1muscle.bioMod",
+        phase_time=0.3,
+        n_shooting=10,
+        min_bound=50,
+    )
+