Removed save and load

bioptim/examples/__main__.py

@@ -50,7 +50,6 @@
                     ("Example mapping", "example_mapping.py"),
                     ("Example multiphase", "example_multiphase.py"),
                     ("Example optimal time", "example_optimal_time.py"),
-                    ("Example save and load", "example_save_and_load.py"),
                     ("Example simulation", "example_simulation.py"),
                     ("Example Implicit Dynamics", "example_implicit_dynamics.py"),
                     ("Pendulum", "pendulum.py"),

bioptim/examples/getting_started/example_simulation.py

@@ -6,7 +6,7 @@
 The second part of the example is to actually solve the program and then simulate the results from this solution.
 The main goal of this kind of simulation, especially in single shooting (that is not resetting the states at each node)
 is to validate the dynamics of multiple shooting. If they both are equal, it usually means that a great confidence
-can be held in the solution. Another goal would be to reload fast a previously saved optimized solution
+can be held in the solution.
 """
 
 from bioptim import InitialGuess, Solution, Shooting, InterpolationType, SolutionIntegrator

bioptim/examples/getting_started/pendulum.py

@@ -8,6 +8,7 @@
 During the optimization process, the graphs are updated real-time (even though it is a bit too fast and short to really
 appreciate it). Finally, once it finished optimizing, it animates the model using the optimal solution
 """
+
 import platform
 
 from bioptim import (

bioptim/examples/moving_horizon_estimation/cyclic_nmpc.py

@@ -69,7 +69,7 @@ def prepare_nmpc(
         dynamics,
         cycle_len,
         cycle_duration,
-        objective_functions=new_objectives,
+        common_objective_functions=new_objectives,
         constraints=constraints,
         x_bounds=x_bound,
         u_bounds=u_bound,

bioptim/examples/moving_horizon_estimation/mhe.py

@@ -155,7 +155,7 @@ def prepare_mhe(
         Dynamics(DynamicsFcn.TORQUE_DRIVEN, expand_dynamics=expand_dynamics, phase_dynamics=phase_dynamics),
         window_len,
         window_duration,
-        objective_functions=new_objectives,
+        common_objective_functions=new_objectives,
         x_bounds=x_bounds,
         u_bounds=u_bounds,
         x_init=x_init_list,

bioptim/examples/moving_horizon_estimation/multi_cyclic_nmpc.py

@@ -86,7 +86,7 @@ def prepare_nmpc(
         cycle_duration=cycle_duration,
         n_cycles_simultaneous=n_cycles_simultaneous,
         n_cycles_to_advance=n_cycles_to_advance,
-        objective_functions=new_objectives,
+        common_objective_functions=new_objectives,
         constraints=constraints,
         x_bounds=x_bounds,
         u_bounds=u_bounds,

bioptim/examples/stochastic_optimal_control/obstacle_avoidance_direct_collocation.py

@@ -184,10 +184,9 @@ def prepare_socp(
     objective_functions = ObjectiveList()
     objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_TIME, weight=1)
     objective_functions.add(
-        ObjectiveFcn.Mayer.MINIMIZE_CONTROL,
+        ObjectiveFcn.Lagrange.MINIMIZE_CONTROL,
         key="u",
         weight=1e-2 / (2 * n_shooting),
-        node=Node.ALL_SHOOTING,
         quadratic=True,
     )
 

bioptim/examples/torque_driven_ocp/trampo_quaternions.py

@@ -86,7 +86,7 @@ def prepare_ocp(
     # Add objective functions
     objective_functions = ObjectiveList()
     objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_MARKERS, marker_index=1, weight=-1)
-    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", node=Node.ALL_SHOOTING, weight=100)
+    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=100)
 
     # Dynamics
     dynamics = DynamicsList()

bioptim/gui/graph.py

@@ -320,7 +320,7 @@ def print(self):
                 print("")
             print("")
             print(f"**********")
-            print(f"MODEL: {self.ocp.original_values['bio_model'][phase_idx]}")
+            print(f"MODEL: {self.ocp.nlp[phase_idx].model.name}")
             if isinstance(self.ocp.nlp[phase_idx].tf, (int, float)):
                 print(f"PHASE DURATION: {round(self.ocp.nlp[phase_idx].tf, 2)} s")
             else:

bioptim/models/biorbd/biorbd_model.py

@@ -29,6 +29,11 @@ def __init__(self, bio_model: str | biorbd.Model, friction_coefficients: np.ndar
         self.model = biorbd.Model(bio_model) if isinstance(bio_model, str) else bio_model
         self._friction_coefficients = friction_coefficients
 
+    @property
+    def name(self) -> str:
+        # parse the path and split to get the .bioMod name
+        return self.model.path().absolutePath().to_string().split("/")[-1]
+
     @property
     def path(self) -> str:
         return self.model.path().relativePath().to_string()

bioptim/models/protocols/biomodel.py

@@ -13,6 +13,11 @@ class BioModel(Protocol):
     As a reminder for developers: only necessary attributes and methods should appear here.
     """
 
+    @property
+    def name(self) -> str:
+        """Get the name of the model"""
+        return ""
+
     def copy(self):
         """copy the model by reloading one"""