Updated black version

src/hippopt/base/dynamics.py

@@ -159,14 +159,16 @@ def equal(
 
     def __eq__(
         self,
-        other: cs.Function
-        | str
-        | list[str]
-        | cs.MX
-        | tuple[cs.Function, dict[str, str]]
-        | tuple[str, dict[str, str]]
-        | tuple[list[str], dict[str, str]]
-        | tuple[cs.MX, dict[str, str]],
+        other: (
+            cs.Function
+            | str
+            | list[str]
+            | cs.MX
+            | tuple[cs.Function, dict[str, str]]
+            | tuple[str, dict[str, str]]
+            | tuple[list[str], dict[str, str]]
+            | tuple[cs.MX, dict[str, str]]
+        ),
     ) -> TDynamics:
         if isinstance(other, tuple):
             return self.equal(f=other[0], names_map=other[1])

src/hippopt/base/multiple_shooting_solver.py

@@ -39,9 +39,9 @@ class MultipleShootingSolver(OptimalControlSolver):
 
     _flattened_variables: FlattenedVariableDict = dataclasses.field(default=None)
 
-    _symbolic_structure: TOptimizationObject | list[
-        TOptimizationObject
-    ] = dataclasses.field(default=None)
+    _symbolic_structure: TOptimizationObject | list[TOptimizationObject] = (
+        dataclasses.field(default=None)
+    )
 
     def __post_init__(
         self,
@@ -363,9 +363,9 @@ def _generate_flattened_and_symbolic_objects(  # TODO: remove some indentation
                     object_in=field_value,
                     top_level=False,
                     base_string=base_string + field.name + ".",
-                    base_iterator=(base_iterator[0], generator)
-                    if generator is not None
-                    else None,
+                    base_iterator=(
+                        (base_iterator[0], generator) if generator is not None else None
+                    ),
                 )
 
                 output_dict = output_dict | inner_dict
@@ -408,9 +408,11 @@ def _generate_flattened_and_symbolic_objects(  # TODO: remove some indentation
                             + "["
                             + str(k)
                             + "].",  # we flatten the list. Note the added [k]
-                            base_iterator=(base_iterator[0], generator)
-                            if generator is not None
-                            else None,
+                            base_iterator=(
+                                (base_iterator[0], generator)
+                                if generator is not None
+                                else None
+                            ),
                         )
 
                         output_dict = output_dict | inner_dict

src/hippopt/base/opti_solver.py

@@ -77,9 +77,9 @@ class OptiSolver(OptimizationSolver):
     _cost_expressions: dict[str, cs.MX] = dataclasses.field(default=None)
     _constraint_expressions: dict[str, cs.MX] = dataclasses.field(default=None)
     _solver: cs.Opti = dataclasses.field(default=None)
-    _output_solution: TOptimizationObject | list[
-        TOptimizationObject
-    ] = dataclasses.field(default=None)
+    _output_solution: TOptimizationObject | list[TOptimizationObject] = (
+        dataclasses.field(default=None)
+    )
     _output_cost: float = dataclasses.field(default=None)
     _cost_values: dict[str, float] = dataclasses.field(default=None)
     _constraint_values: dict[str, np.ndarray] = dataclasses.field(default=None)
@@ -304,9 +304,11 @@ def _get_opti_solution(
 
     def _generate_solution_output(
         self,
-        variables: TOptimizationObject
-        | list[TOptimizationObject]
-        | list[list[TOptimizationObject]],
+        variables: (
+            TOptimizationObject
+            | list[TOptimizationObject]
+            | list[list[TOptimizationObject]]
+        ),
         input_solution: cs.OptiSol | dict,
     ) -> TOptimizationObject | list[TOptimizationObject]:
         output = copy.deepcopy(variables)
@@ -383,12 +385,16 @@ def _set_opti_guess(self, variable: cs.MX, value: np.ndarray) -> None:
 
     def _set_initial_guess_internal(
         self,
-        initial_guess: TOptimizationObject
-        | list[TOptimizationObject]
-        | list[list[TOptimizationObject]],
-        corresponding_variable: TOptimizationObject
-        | list[TOptimizationObject]
-        | list[list[TOptimizationObject]],
+        initial_guess: (
+            TOptimizationObject
+            | list[TOptimizationObject]
+            | list[list[TOptimizationObject]]
+        ),
+        corresponding_variable: (
+            TOptimizationObject
+            | list[TOptimizationObject]
+            | list[list[TOptimizationObject]]
+        ),
         base_name: str = "",
     ) -> None:
         if isinstance(initial_guess, list):
@@ -679,12 +685,16 @@ def solve(self) -> None:
                 opti=self._solver,
                 variables=variables,
                 parameters=parameters,
-                costs=list(self._cost_expressions.values())
-                if self._callback_save_costs
-                else [],
-                constraints=list(self._constraint_expressions.values())
-                if self._callback_save_constraint_multipliers
-                else [],
+                costs=(
+                    list(self._cost_expressions.values())
+                    if self._callback_save_costs
+                    else []
+                ),
+                constraints=(
+                    list(self._constraint_expressions.values())
+                    if self._callback_save_constraint_multipliers
+                    else []
+                ),
             )
             self._solver.callback(self._callback)
         try:

src/hippopt/base/optimal_control_problem.py

@@ -48,9 +48,9 @@ def __iter__(self):
 
 @dataclasses.dataclass
 class OptimalControlProblem(Problem[TOptimalControlSolver, TInputObjects]):
-    optimal_control_solver: dataclasses.InitVar[
-        OptimalControlSolver
-    ] = dataclasses.field(default=None)
+    optimal_control_solver: dataclasses.InitVar[OptimalControlSolver] = (
+        dataclasses.field(default=None)
+    )
 
     def __post_init__(
         self,

src/hippopt/base/problem.py

@@ -39,9 +39,9 @@ class Output(Generic[TGenericOptimizationObject]):
     _cost_values: dataclasses.InitVar[dict[str, np.ndarray]] = dataclasses.field(
         default=None
     )
-    _constraint_multipliers: dataclasses.InitVar[
-        dict[str, np.ndarray]
-    ] = dataclasses.field(default=None)
+    _constraint_multipliers: dataclasses.InitVar[dict[str, np.ndarray]] = (
+        dataclasses.field(default=None)
+    )
 
     def __post_init__(
         self,

src/hippopt/robot_planning/utilities/foot_contact_state_plotter.py

@@ -25,9 +25,9 @@ class ContactPointStatePlotterSettings:
     force_axes: matplotlib.axes.Axes | None = dataclasses.field(default=None)
     terrain: TerrainDescriptor = dataclasses.field(default=None)
 
-    input_complementarity_axes: dataclasses.InitVar[
-        list[matplotlib.axes.Axes]
-    ] = dataclasses.field(default=None)
+    input_complementarity_axes: dataclasses.InitVar[list[matplotlib.axes.Axes]] = (
+        dataclasses.field(default=None)
+    )
     input_force_axes: dataclasses.InitVar[matplotlib.axes.Axes] = dataclasses.field(
         default=None
     )

src/hippopt/robot_planning/variables/humanoid.py

@@ -30,9 +30,9 @@ class HumanoidState(OptimizationObject):
 
     com: StorageType = default_storage_field(OverridableVariable)
 
-    contact_point_descriptors: dataclasses.InitVar[
-        FeetContactPointDescriptors
-    ] = dataclasses.field(default=None)
+    contact_point_descriptors: dataclasses.InitVar[FeetContactPointDescriptors] = (
+        dataclasses.field(default=None)
+    )
     number_of_joints: dataclasses.InitVar[int] = dataclasses.field(default=None)
 
     def __post_init__(

test/test_opti_generate_objects.py

@@ -178,10 +178,10 @@ class CustomCustomOverridableVariableInner(OptimizationObject):
 
 @dataclasses.dataclass
 class CustomCustomOverridableVariableNested(OptimizationObject):
-    composite: CompositeType[
-        CustomCustomOverridableVariableInner
-    ] = default_composite_field(
-        cls=OverridableParameter, factory=CustomCustomOverridableVariableInner
+    composite: CompositeType[CustomCustomOverridableVariableInner] = (
+        default_composite_field(
+            cls=OverridableParameter, factory=CustomCustomOverridableVariableInner
+        )
     )