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pyomeca · Sep 23, 2024 · f275729 · f275729
1 parent c78098e
commit f275729
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Showing 4 changed files with 6 additions and 15 deletions.
diff --git a/bioptim/dynamics/configure_new_variable.py b/bioptim/dynamics/configure_new_variable.py
@@ -640,4 +640,4 @@ def append_faked_optim_var(name: str, optim_var, keys: list):
         to_second.extend(list(np.array(optim_var[key].mapping.to_second.map_idx) + len(to_second)))
         to_first.extend(list(np.array(optim_var[key].mapping.to_first.map_idx) + len(to_first)))
 
-    optim_var.append_fake(name, index, None, BiMapping(to_second, to_first))
+    optim_var.append_fake(name, index, BiMapping(to_second, to_first))
diff --git a/bioptim/dynamics/configure_problem.py b/bioptim/dynamics/configure_problem.py
@@ -1496,15 +1496,18 @@ def configure_integrated_value(
         initial_vector = StochasticBioModel.reshape_to_vector(initial_matrix)
         cx_scaled_next_formatted = [initial_vector for _ in range(n_cx)]
         nlp.integrated_values.append(
-            name, cx_scaled_next_formatted, cx_scaled_next_formatted, initial_matrix, dummy_mapping, 0
+            name=name,
+            cx=cx_scaled_next_formatted,
+            cx_scaled=[initial_matrix for i in range(n_cx)],  # Only the first value is used
+            mapping=dummy_mapping,
+            node_index=0
         )
         for node_index in range(1, nlp.ns + 1):  # cannot use phase_dynamics == PhaseDynamics.SHARED_DURING_THE_PHASE
             cx_scaled_next = nlp.integrated_value_functions[name](nlp, node_index)
             cx_scaled_next_formatted = [cx_scaled_next for _ in range(n_cx)]
             nlp.integrated_values.append(
                 name,
                 cx_scaled_next_formatted,
-                cx_scaled_next_formatted,
                 cx_scaled_next,
                 dummy_mapping,
                 node_index,

diff --git a/bioptim/examples/muscle_driven_ocp/muscle_excitations_tracker.py b/bioptim/examples/muscle_driven_ocp/muscle_excitations_tracker.py
@@ -114,23 +114,20 @@ def generate_data(
             name="q",
             cx=[symbolic_q, symbolic_q, symbolic_q],
             cx_scaled=[symbolic_q, symbolic_q, symbolic_q],
-            mx=symbolic_q,
             mapping=nlp.variable_mappings["q"],
             node_index=node_index,
         )
         nlp.states.append(
             name="qdot",
             cx=[symbolic_qdot, symbolic_qdot, symbolic_qdot],
             cx_scaled=[symbolic_qdot, symbolic_qdot, symbolic_qdot],
-            mx=symbolic_qdot,
             mapping=nlp.variable_mappings["qdot"],
             node_index=node_index,
         )
         nlp.states.append(
             name="muscles",
             cx=[symbolic_mus_states, symbolic_mus_states, symbolic_mus_states],
             cx_scaled=[symbolic_mus_states, symbolic_mus_states, symbolic_mus_states],
-            mx=symbolic_mus_states,
             mapping=nlp.variable_mappings["muscles"],
             node_index=node_index,
         )
@@ -139,31 +136,27 @@ def generate_data(
             name="tau",
             cx=[symbolic_tau, symbolic_tau, symbolic_tau],
             cx_scaled=[symbolic_tau, symbolic_tau, symbolic_tau],
-            mx=symbolic_tau,
             mapping=nlp.variable_mappings["tau"],
             node_index=node_index,
         )
         nlp.controls.append(
             name="muscles",
             cx=[symbolic_mus_controls, symbolic_mus_controls, symbolic_mus_controls],
             cx_scaled=[symbolic_mus_controls, symbolic_mus_controls, symbolic_mus_controls],
-            mx=symbolic_mus_controls,
             mapping=nlp.variable_mappings["muscles"],
             node_index=node_index,
         )
         nlp.states_dot.append(
             name="qdot",
             cx=[symbolic_qdot, symbolic_qdot, symbolic_qdot],
             cx_scaled=[symbolic_qdot, symbolic_qdot, symbolic_qdot],
-            mx=symbolic_qdot,
             mapping=nlp.variable_mappings["qdot"],
             node_index=node_index,
         )
         nlp.states_dot.append(
             name="qddot",
             cx=[symbolic_qddot, symbolic_qddot, symbolic_qddot],
             cx_scaled=[symbolic_qddot, symbolic_qddot, symbolic_qddot],
-            mx=symbolic_qddot,
             mapping=nlp.variable_mappings["qddot"],
             node_index=node_index,
         )

diff --git a/bioptim/optimization/optimal_control_program.py b/bioptim/optimization/optimal_control_program.py
@@ -1704,15 +1704,10 @@ def _define_numerical_timeseries(self, dynamics):
                             f"{key}_phase{i_phase}_{i_component}_cx",
                             variable_shape[0],
                         )
-                        mx = MX.sym(
-                            f"{key}_phase{i_phase}_{i_component}_mx",
-                            variable_shape[0],
-                        )
 
                         numerical_timeseries[-1].append(
                             name=f"{key}_{i_component}",
                             cx=[cx, cx, cx],
-                            mx=mx,
                             bimapping=BiMapping(
                                 Mapping(list(range(variable_shape[0]))), Mapping(list(range(variable_shape[0])))
                             ),