Merge pull request #880 from pariterre/master

Fixed ACADOS for Macos

.github/workflows/run_tests_osx_win.yml

@@ -22,7 +22,7 @@ jobs:
     steps:
       - name: Set prefix osx
         run: |
-          echo "PREFIX=${{ env.PREFIX_macOS }}" >> $GITHUB_ENV
+          echo "PREFIX=${{ env.PREFIX_MACOS }}" >> $GITHUB_ENV
         if: matrix.os == 'macos-latest'
 
       - name: Set prefix windows

bioptim/dynamics/configure_new_variable.py

@@ -223,7 +223,7 @@ def check_variable_copy_condition(
             and name in getattr(nlp[use_from_phase_idx], decision_variable_attribute)
         )
 
-    def define_cx_scaled(self, n_col: int, n_shooting: int, initial_node) -> list[MX | SX, ...]:
+    def define_cx_scaled(self, n_col: int, n_shooting: int, initial_node) -> list[MX | SX]:
         """
         This function defines the decision variables, either MX or SX,
         scaled to the physical world, they mean something according to the physical model considered.
@@ -259,7 +259,7 @@ def define_cx_scaled(self, n_col: int, n_shooting: int, initial_node) -> list[MX
                     )
         return _cx
 
-    def define_cx_unscaled(self, _cx_scaled: list[MX | SX, ...], scaling: np.ndarray) -> list[MX | SX, ...]:
+    def define_cx_unscaled(self, _cx_scaled: list[MX | SX], scaling: np.ndarray) -> list[MX | SX]:
         """
         This function defines the decision variables, either MX or SX,
         unscaled means here the decision variable doesn't correspond to physical quantity.
@@ -269,7 +269,7 @@ def define_cx_unscaled(self, _cx_scaled: list[MX | SX, ...], scaling: np.ndarray
 
         Parameters
         ---------
-        _cx_scaled: list[MX | SX, ...]
+        _cx_scaled: list[MX | SX]
             Decision variables scaled to the physical world
         scaling: np.ndarray
             The scaling factors associated to the decision variable

bioptim/examples/getting_started/example_multinode_constraints.py

@@ -31,7 +31,7 @@
 
 
 def custom_multinode_constraint(
-    controllers: list[PenaltyController, ...], coef: float, states_mapping: BiMapping = None
+    controllers: list[PenaltyController], coef: float, states_mapping: BiMapping = None
 ) -> MX:
     """
     The constraint of the transition. The values from the end of the phase to the next are multiplied by coef to
@@ -42,7 +42,7 @@ def custom_multinode_constraint(
 
     Parameters
     ----------
-    controllers: list[PenaltyController, ...]
+    controllers: list[PenaltyController]
         All the controller for the penalties
     coef: float
         The coefficient of the phase transition (makes no physical sens)

bioptim/gui/plot.py

@@ -778,7 +778,7 @@ def _compute_y_from_plot_func(
             The custom plot to compute
         phase_idx: int
             The index of the current phase
-        time_stepwise: list[list[DM], ...]
+        time_stepwise: list[list[DM]]
             The time vector of each phase
         dt
             The delta times of the current phase

bioptim/interfaces/acados_interface.py

@@ -851,9 +851,9 @@ def get_optimized_value(self) -> list | dict:
         out = {
             "x": [],
             "u": acados_u,
-            "solver_time_to_optimize": self.ocp_solver.get_stats("time_tot")[0],
+            "solver_time_to_optimize": self.ocp_solver.get_stats("time_tot"),
             "real_time_to_optimize": self.real_time_to_optimize,
-            "iter": self.ocp_solver.get_stats("sqp_iter")[0],
+            "iter": self.ocp_solver.get_stats("sqp_iter"),
             "status": self.status,
             "solver": SolverType.ACADOS,
         }

bioptim/limits/constraints.py

@@ -103,7 +103,7 @@ def add_or_replace_to_penalty_pool(self, ocp, nlp):
         elif self.bounds.shape[0] != len(self.rows):
             raise RuntimeError(f"bounds rows is {self.bounds.shape[0]} but should be {self.rows} or empty")
 
-    def _add_penalty_to_pool(self, controller: list[PenaltyController, ...]):
+    def _add_penalty_to_pool(self, controller: list[PenaltyController]):
         controller = controller[0]  # This is a special case of Node.TRANSITION
 
         if self.penalty_type == PenaltyType.INTERNAL:
@@ -1222,7 +1222,7 @@ def add_or_replace_to_penalty_pool(self, ocp, nlp):
         elif self.bounds.shape[0] != len(self.rows):
             raise RuntimeError(f"bounds rows is {self.bounds.shape[0]} but should be {self.rows} or empty")
 
-    def _add_penalty_to_pool(self, controller: list[PenaltyController, ...]):
+    def _add_penalty_to_pool(self, controller: list[PenaltyController]):
         controller = controller[0]  # This is a special case of Node.TRANSITION
 
         if self.penalty_type == PenaltyType.INTERNAL:

bioptim/limits/multinode_penalty.py

@@ -85,7 +85,7 @@ def __init__(
     def _get_pool_to_add_penalty(self, ocp, nlp):
         raise NotImplementedError("This is an abstract method and should be implemented by child")
 
-    def _add_penalty_to_pool(self, controller: list[PenaltyController, ...]):
+    def _add_penalty_to_pool(self, controller: list[PenaltyController]):
 
         controller = controller[0]  # This is a special case of Node.TRANSITION
 
@@ -124,9 +124,9 @@ class Functions:
         @staticmethod
         def states_equality(
             penalty,
-            controllers: list[PenaltyController, ...],
+            controllers: list[PenaltyController],
             key: str = "all",
-            states_mapping: list[BiMapping, ...] = None,
+            states_mapping: list[BiMapping] = None,
         ):
             """
             The most common continuity function, that is state before equals state after
@@ -171,15 +171,15 @@ def states_equality(
             return out
 
         @staticmethod
-        def controls_equality(penalty, controllers: list[PenaltyController, ...], key: str = "all"):
+        def controls_equality(penalty, controllers: list[PenaltyController], key: str = "all"):
             """
             The controls before equals controls after
 
             Parameters
             ----------
             penalty : MultinodePenalty
                 A reference to the penalty
-            controllers: list[PenaltyController, ...]
+            controllers: list[PenaltyController]
                     The penalty node elements
 
             Returns
@@ -210,7 +210,7 @@ def controls_equality(penalty, controllers: list[PenaltyController, ...], key: s
         @staticmethod
         def algebraic_states_equality(
             penalty,
-            controllers: list[PenaltyController, ...],
+            controllers: list[PenaltyController],
             key: str = "all",
         ):
             """
@@ -249,15 +249,15 @@ def algebraic_states_equality(
             return out
 
         @staticmethod
-        def com_equality(penalty, controllers: list[PenaltyController, ...]):
+        def com_equality(penalty, controllers: list[PenaltyController]):
             """
             The centers of mass are equals for the specified phases and the specified nodes
 
             Parameters
             ----------
             penalty : MultinodePenalty
                 A reference to the penalty
-            controllers: list[PenaltyController, ...]
+            controllers: list[PenaltyController]
                     The penalty node elements
 
             Returns
@@ -277,15 +277,15 @@ def com_equality(penalty, controllers: list[PenaltyController, ...]):
             return out
 
         @staticmethod
-        def com_velocity_equality(penalty, controllers: list[PenaltyController, ...]):
+        def com_velocity_equality(penalty, controllers: list[PenaltyController]):
             """
             The centers of mass velocity are equals for the specified phases and the specified nodes
 
             Parameters
             ----------
             penalty : MultinodePenalty
                 A reference to the penalty
-            controllers: list[PenaltyController, ...]
+            controllers: list[PenaltyController]
                     The penalty node elements
 
             Returns
@@ -651,7 +651,7 @@ def custom(penalty, controllers: list[PenaltyController, PenaltyController], **e
             return penalty.custom_function(controllers, **extra_parameters)
 
         @staticmethod
-        def _prepare_controller_cx(penalty, controllers: list[PenaltyController, ...]):
+        def _prepare_controller_cx(penalty, controllers: list[PenaltyController]):
             """
             This calls the _compute_controller_cx function for each of the controller then dispatch the cx appropriately
             to the controllers
@@ -667,7 +667,7 @@ def _prepare_controller_cx(penalty, controllers: list[PenaltyController, ...]):
                 c.cx_index_to_get = index
 
         @staticmethod
-        def _prepare_states_mapping(controllers: list[PenaltyController, ...], states_mapping: list[BiMapping, ...]):
+        def _prepare_states_mapping(controllers: list[PenaltyController], states_mapping: list[BiMapping]):
             """
             Prepare a new state_mappings if None is sent. Otherwise, it simply returns the current states_mapping
 

bioptim/limits/penalty_option.py

@@ -67,15 +67,15 @@ class PenaltyOption(OptionGeneric):
     _check_target_dimensions(self, controller: PenaltyController, n_frames: int)
         Checks if the variable index is consistent with the requested variable.
         If the function returns, all is okay
-    _set_penalty_function(self, controller: list[PenaltyController, ...], fcn: MX | SX)
+    _set_penalty_function(self, controller: list[PenaltyController], fcn: MX | SX)
         Finalize the preparation of the penalty (setting function and weighted_function)
     add_target_to_plot(self, controller: PenaltyController, combine_to: str)
         Interface to the plot so it can be properly added to the proper plot
     _finish_add_target_to_plot(self, controller: PenaltyController)
         Internal interface to add (after having check the target dimensions) the target to the plot if needed
     add_or_replace_to_penalty_pool(self, ocp, nlp)
         Doing some configuration on the penalty and add it to the list of penalty
-    _add_penalty_to_pool(self, controller: list[PenaltyController, ...])
+    _add_penalty_to_pool(self, controller: list[PenaltyController])
         Return the penalty pool for the specified penalty (abstract)
     ensure_penalty_sanity(self, ocp, nlp)
         Resets a penalty. A negative penalty index creates a new empty penalty (abstract)
@@ -332,7 +332,7 @@ def transform_penalty_to_stochastic(self, controller: PenaltyController, fcn, st
 
         return diag(fcn_variation)
 
-    def _set_phase_dynamics(self, controllers: list[PenaltyController, ...]):
+    def _set_phase_dynamics(self, controllers: list[PenaltyController]):
         phase_dynamics = [c.get_nlp.phase_dynamics for c in controllers]
         if self.phase_dynamics:
             # If it was already set (e.g. for multinode), we want to make sure it is consistent

bioptim/limits/phase_transition.py

@@ -142,7 +142,7 @@ class Functions:
         def continuous(
             transition,
             controllers: list[PenaltyController, PenaltyController],
-            states_mapping: list[BiMapping, ...] = None,
+            states_mapping: list[BiMapping] = None,
         ):
             """
             The most common continuity function, that is state before equals state after
@@ -173,7 +173,7 @@ def continuous(
         def continuous_controls(
             transition,
             controllers: list[PenaltyController, PenaltyController],
-            controls_mapping: list[BiMapping, ...] = None,
+            controls_mapping: list[BiMapping] = None,
         ):
             """
             This continuity function is only relevant for ControlType.LINEAR_CONTINUOUS otherwise don't use it.

bioptim/models/biorbd/multi_biorbd_model.py

@@ -725,7 +725,7 @@ def ligament_joint_torque(self, q, qdot) -> MX:
     def ranges_from_model(self, variable: str):
         return [the_range for model in self.models for the_range in model.ranges_from_model(variable)]
 
-    def bounds_from_ranges(self, variables: str | list[str, ...], mapping: BiMapping | BiMappingList = None) -> Bounds:
+    def bounds_from_ranges(self, variables: str | list[str], mapping: BiMapping | BiMappingList = None) -> Bounds:
         return bounds_from_ranges(self, variables, mapping)
 
     def _var_mapping(self, key: str, range_for_mapping: int | list | tuple | range, mapping: BiMapping = None) -> dict:

bioptim/models/protocols/biomodel.py

@@ -260,7 +260,7 @@ def passive_joint_torque(self, q, qdot) -> MX:
     def ligament_joint_torque(self, q, qdot) -> MX:
         """Get the ligament joint torque"""
 
-    def bounds_from_ranges(self, variables: str | list[str, ...], mapping: BiMapping | BiMappingList = None) -> Bounds:
+    def bounds_from_ranges(self, variables: str | list[str], mapping: BiMapping | BiMappingList = None) -> Bounds:
         """
         Create bounds from ranges of the model depending on the variable chosen, such as q, qdot, qddot
 
@@ -325,11 +325,7 @@ def partitioned_forward_dynamics(
 
     @staticmethod
     def animate(
-        ocp,
-        solution: "SolutionData",
-        show_now: bool = True,
-        tracked_markers: list[np.ndarray, ...] = None,
-        **kwargs: Any
+        ocp, solution: "SolutionData", show_now: bool = True, tracked_markers: list[np.ndarray] = None, **kwargs: Any
     ) -> None | list:
         """
         Animate a solution
@@ -340,7 +336,7 @@ def animate(
             The solution to animate
         show_now: bool
             If the animation should be shown immediately or not
-        tracked_markers: list[np.ndarray, ...]
+        tracked_markers: list[np.ndarray]
             The tracked markers (3, n_markers, n_frames)
         kwargs: dict
             The options to pass to the animator

bioptim/models/utils.py

@@ -62,7 +62,7 @@ def bounds_from_ranges(model, key: str, mapping: BiMapping | BiMappingList = Non
     ----------
     model: bio_model
         such as BiorbdModel or MultiBiorbdModel
-    key: str | list[str, ...]
+    key: str | list[str]
         The variables to generate the bounds from, such as "q", "qdot", "qddot", or ["q", "qdot"],
     mapping: BiMapping | BiMappingList
         The mapping to use to generate the bounds. If None, the default mapping is built

bioptim/optimization/optimization_variable.py

@@ -39,7 +39,7 @@ def __init__(
         name: str,
         mx: MX,
         cx_start: list | None,
-        index: [range, list],
+        index: range | list,
         mapping: BiMapping = None,
         parent_list=None,
     ):
@@ -50,15 +50,15 @@ def __init__(
             The name of the variable
         mx: MX
             The MX variable associated with this variable
-        index: [range, list]
+        index: range | list
             The indices to find this variable
         parent_list: OptimizationVariableList
             The list the OptimizationVariable is in
         """
         self.name: str = name
         self.mx: MX = mx
         self.original_cx: list = cx_start
-        self.index: [range, list] = index
+        self.index: range | list = index
         self.mapping: BiMapping = mapping
         self.parent_list: OptimizationVariableList = parent_list
 
@@ -494,8 +494,8 @@ def __init__(self, phase_dynamics: PhaseDynamics):
             user sets it to something else)
         """
         self.cx_constructor = None
-        self._unscaled: list[OptimizationVariableList, ...] = []
-        self._scaled: list[OptimizationVariableList, ...] = []
+        self._unscaled: list[OptimizationVariableList] = []
+        self._scaled: list[OptimizationVariableList] = []
         self._node_index = 0  # TODO: [0] to [node_index]
         self.phase_dynamics = phase_dynamics