remove useless helper.

bioptim/optimization/optimal_control_program.py

@@ -2019,52 +2019,3 @@ def _scale_values(values, scaling_entities, penalty, scaling_data):
         scaling = np.repeat(complete_scaling, number_of_repeat, axis=0)
 
     return values / scaling
-
-
-def concatenate_optimization_variables(
-    variable: list[np.ndarray] | np.ndarray,
-    continuous_phase: bool = True,
-    continuous_interval: bool = True,
-    merge_phases: bool = True,
-) -> np.ndarray | list[dict[np.ndarray]]:
-    """
-    # TODO: remove from here I don't this function to be here forever, duplicated of solution helper.
-    This function concatenates the decision variables of the phases of the system
-    into a single array, omitting the last element of each phase except for the last one.
-
-    Parameters
-    ----------
-    variable : list or dict
-        list of decision variables of the phases of the system
-    continuous_phase: bool
-        If the arrival value of a node should be discarded [True] or kept [False]. The value of an integrated
-    continuous_interval: bool
-        If the arrival value of a node of each interval should be discarded [True] or kept [False].
-        Only useful in direct multiple shooting
-    merge_phases: bool
-        If the decision variables of each phase should be merged into a single array [True] or kept separated [False].
-
-    Returns
-    -------
-    z_concatenated : np.ndarray or dict
-        array of the decision variables of the phases of the system concatenated
-    """
-    if len(variable[0].shape):
-        if isinstance(variable[0][0], np.ndarray):
-            z_final = []
-            for zi in variable:
-                z_final.append(concatenate_optimization_variables(zi, continuous_interval))
-
-            if merge_phases:
-                return concatenate_optimization_variables(z_final, continuous_phase)
-            else:
-                return z_final
-        else:
-            final_tuple = []
-            for i, y in enumerate(variable):
-                if i < (len(variable) - 1) and continuous_phase:
-                    final_tuple.append(y[:, :-1] if len(y.shape) == 2 else y[:-1])
-                else:
-                    final_tuple.append(y)
-
-        return np.hstack(final_tuple)