Removed "..." in type hints of list

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/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
 

bioptim/optimization/solution/solution.py

@@ -373,7 +373,7 @@ def decision_time(
 
         Parameters
         ----------
-        to_merge: SolutionMerge | list[SolutionMerge, ...]
+        to_merge: SolutionMerge | list[SolutionMerge]
             The type of merge to perform. If None, then no merge is performed. It is often useful to merge NODES, but
             is completely useless to merge KEYS
         time_alignment: TimeAlignment
@@ -404,7 +404,7 @@ def stepwise_time(
 
         Parameters
         ----------
-        to_merge: SolutionMerge | list[SolutionMerge, ...]
+        to_merge: SolutionMerge | list[SolutionMerge]
             The type of merge to perform. If None, then no merge is performed. It is often useful to merge NODES, but
             is completely useless to merge KEYS
         time_alignment: TimeAlignment
@@ -533,7 +533,7 @@ def decision_states(self, scaled: bool = False, to_merge: SolutionMerge | list[S
         scaled: bool
             If the decision states should be scaled or not (note that scaled is as Ipopt received them, while unscaled
             is as the model needs temps). If you don't know what it means, you probably want the unscaled version.
-        to_merge: SolutionMerge | list[SolutionMerge, ...]
+        to_merge: SolutionMerge | list[SolutionMerge]
             The type of merge to perform. If None, then no merge is performed.
 
         Returns
@@ -555,7 +555,7 @@ def stepwise_states(self, scaled: bool = False, to_merge: SolutionMerge | list[S
         scaled: bool
             If the states should be scaled or not (note that scaled is as Ipopt received them, while unscaled is as the
             model needs temps). If you don't know what it means, you probably want the unscaled version.
-        to_merge: SolutionMerge | list[SolutionMerge, ...]
+        to_merge: SolutionMerge | list[SolutionMerge]
             The type of merge to perform. If None, then no merge is performed.
 
         Returns
@@ -580,7 +580,7 @@ def decision_controls(self, scaled: bool = False, to_merge: SolutionMerge | list
         scaled : bool
             If the decision controls should be scaled or not (note that scaled is as Ipopt received them, while unscaled
             is as the model needs temps). If you don't know what it means, you probably want the unscaled version.
-        to_merge : SolutionMerge | list[SolutionMerge, ...]
+        to_merge : SolutionMerge | list[SolutionMerge]
             The type of merge to perform. If None, then no merge is performed.
         """
         return self.stepwise_controls(scaled=scaled, to_merge=to_merge)
@@ -594,7 +594,7 @@ def stepwise_controls(self, scaled: bool = False, to_merge: SolutionMerge | list
         scaled: bool
             If the controls should be scaled or not (note that scaled is as Ipopt received them, while unscaled is as
             the model needs temps). If you don't know what it means, you probably want the unscaled version.
-        to_merge: SolutionMerge | list[SolutionMerge, ...]
+        to_merge: SolutionMerge | list[SolutionMerge]
             The type of merge to perform. If None, then no merge is performed.
 
         Returns
@@ -660,7 +660,7 @@ def decision_algebraic_states(self, scaled: bool = False, to_merge: SolutionMerg
         scaled: bool
             If the decision states should be scaled or not (note that scaled is as Ipopt received them, while unscaled
             is as the model needs temps). If you don't know what it means, you probably want the unscaled version.
-        to_merge: SolutionMerge | list[SolutionMerge, ...]
+        to_merge: SolutionMerge | list[SolutionMerge]
             The type of merge to perform. If None, then no merge is performed.
 
         Returns
@@ -769,7 +769,7 @@ def integrate(
             The integration shooting type to use
         integrator: SolutionIntegrator
             The type of integrator to use
-        to_merge: SolutionMerge | list[SolutionMerge, ...]
+        to_merge: SolutionMerge | list[SolutionMerge]
             The type of merge to perform. If None, then no merge is performed.
         duplicated_times: bool
             If the times should be duplicated for each node.
@@ -1096,7 +1096,7 @@ def _return_time_vector(self, to_merge: SolutionMerge | list[SolutionMerge], dup
         Returns the time vector at each node that matches stepwise_states or stepwise_controls
         Parameters
         ----------
-        to_merge: SolutionMerge | list[SolutionMerge, ...]
+        to_merge: SolutionMerge | list[SolutionMerge]
             The merge type to perform. If None, then no merge is performed.
         duplicated_times: bool
             If the times should be duplicated for each node.