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pyomeca · Oct 8, 2024 · bd5a025 · bd5a025
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Showing 4 changed files with 16 additions and 4 deletions.
diff --git a/bioptim/examples/moving_horizon_estimation/multi_cyclic_nmpc_with_parameters.py b/bioptim/examples/moving_horizon_estimation/multi_cyclic_nmpc_with_parameters.py
@@ -38,6 +38,7 @@
     VariableScaling,
 )
 
+
 class MyCyclicNMPC(MultiCyclicNonlinearModelPredictiveControl):
     def advance_window_bounds_states(self, sol, n_cycles_simultaneous=None):
         # Reimplementation of the advance_window method so the rotation of the wheel restart at -pi
@@ -103,7 +104,9 @@ def parameter_dependent_dynamic(
     return DynamicsEvaluation(dxdt=vertcat(dq, ddq), defects=None)
 
 
-def custom_configure(ocp: OptimalControlProgram, nlp: NonLinearProgram, numerical_data_timeseries: dict[str, np.ndarray] = None):
+def custom_configure(
+    ocp: OptimalControlProgram, nlp: NonLinearProgram, numerical_data_timeseries: dict[str, np.ndarray] = None
+):
     """
     Tell the program which variables are states and controls.
     The user is expected to use the ConfigureProblem.configure_xxx functions.

diff --git a/bioptim/examples/torque_driven_ocp/example_pendulum_time_dependent.py b/bioptim/examples/torque_driven_ocp/example_pendulum_time_dependent.py
@@ -81,7 +81,9 @@ def time_dependent_dynamic(
     return DynamicsEvaluation(dxdt=vertcat(dq, ddq), defects=None)
 
 
-def custom_configure(ocp: OptimalControlProgram, nlp: NonLinearProgram, numerical_data_timeseries: dict[str, np.ndarray] = None):
+def custom_configure(
+    ocp: OptimalControlProgram, nlp: NonLinearProgram, numerical_data_timeseries: dict[str, np.ndarray] = None
+):
     """
     Tell the program which variables are states and controls.
     The user is expected to use the ConfigureProblem.configure_xxx functions.

diff --git a/bioptim/optimization/receding_horizon_optimization.py b/bioptim/optimization/receding_horizon_optimization.py
@@ -847,7 +847,12 @@ def _initialize_one_cycle(self, dt: float, states: np.ndarray, controls: np.ndar
                 size=self.nlp[0].parameters[key].shape,
                 scaling=self.nlp[0].parameters[key].scaling,
             )
-            p_init.add(key, parameters_tp, interpolation=InterpolationType.EACH_FRAME, phase=0,)
+            p_init.add(
+                key,
+                parameters_tp,
+                interpolation=InterpolationType.EACH_FRAME,
+                phase=0,
+            )
 
         solution_ocp = OptimalControlProgram(
             bio_model=model_class(**model_initializer),

diff --git a/tests/shard2/test_global_nmpc_final.py b/tests/shard2/test_global_nmpc_final.py
@@ -191,7 +191,9 @@ def update_functions(_nmpc, cycle_idx, _sol):
 
     # initial and final parameters
     for key in parameters.keys():
-        npt.assert_almost_equal(parameters[key], [np.array([2.5594204]), np.array([2.55932934]), np.array([2.56215198])])
+        npt.assert_almost_equal(
+            parameters[key], [np.array([2.5594204]), np.array([2.55932934]), np.array([2.56215198])]
+        )
 
     # check time
     n_steps = nmpc.nlp[0].ode_solver.n_integration_steps