pyomeca#746 Issue Fixed

bioptim/examples/stochastic_optimal_control/arm_reaching_muscle_driven.py

@@ -298,7 +298,7 @@ def expected_feedback_effort(controllers: list[PenaltyController], sensory_noise
  controllers[0].controls.cx_start,
  controllers[0].parameters.cx_start,
  controllers[0].stochastic_variables.cx_start,
- controllers[0].get_nlp,
+ controllers[0].nlp,
  )
  trace_k_sensor_k = cas.trace(k_matrix @ sensory_noise_matrix @ k_matrix.T)
  e_fb = k_matrix @ ((hand_pos_velo - ref) + sensory_noise_magnitude)
@@ -329,7 +329,7 @@ def zero_acceleration(controller: PenaltyController, force_field_magnitude: floa
  controller.controls.cx_start,
  controller.parameters.cx_start,
  controller.stochastic_variables.cx_start,
- controller.get_nlp,
+ controller.nlp,
  force_field_magnitude=force_field_magnitude,
  with_noise=False,
  )

bioptim/examples/stochastic_optimal_control/arm_reaching_torque_driven_explicit.py

@@ -299,7 +299,7 @@ def expected_feedback_effort(controllers: list[PenaltyController]) -> cas.MX:
  controllers[0].controls.cx_start,
  controllers[0].parameters.cx_start,
  controllers[0].stochastic_variables.cx_start,
- controllers[0].get_nlp,
+ controllers[0].nlp,
  )
  e_fb = k_matrix @ ((estimated_ref - ref) + controllers[0].model.sensory_noise_magnitude)
  jac_e_fb_x = cas.jacobian(e_fb, controllers[0].states.cx_start)

bioptim/limits/constraints.py

@@ -123,18 +123,18 @@ def _add_penalty_to_pool(self, controller: PenaltyController):
 
  if self.penalty_type == PenaltyType.INTERNAL:
  pool = (
- controller.get_nlp.g_internal
- if controller is not None and controller.get_nlp
+ controller.nlp.g_internal
+ if controller is not None and controller.nlp
  else controller.ocp.g_internal
  )
  elif self.penalty_type == ConstraintType.IMPLICIT:
  pool = (
- controller.get_nlp.g_implicit
- if controller is not None and controller.get_nlp
+ controller.nlp.g_implicit
+ if controller is not None and controller.nlp
  else controller.ocp.g_implicit
  )
  elif self.penalty_type == PenaltyType.USER:
- pool = controller.get_nlp.g if controller is not None and controller.get_nlp else controller.ocp.g
+ pool = controller.nlp.g if controller is not None and controller.nlp else controller.ocp.g
  else:
  raise ValueError(f"Invalid constraint type {self.penalty_type}.")
  pool[self.list_index] = self
@@ -267,7 +267,7 @@ def non_slipping(
  constraint.min_bound = np.array([0, 0])
  constraint.max_bound = np.array([np.inf, np.inf])
 
- contact = controller.get_nlp.contact_forces_func(
+ contact = controller.nlp.contact_forces_func(
  controller.time.cx,
  controller.states.cx_start,
  controller.controls.cx_start,
@@ -338,7 +338,7 @@ def torque_max_from_q_and_qdot(
  n_rows = value.shape[0] // 2
  else:
  if (
- controller.get_nlp.phase_dynamics == PhaseDynamics.ONE_PER_NODE
+ controller.nlp.phase_dynamics == PhaseDynamics.ONE_PER_NODE
  and not isinstance(constraint.rows, int)
  and len(constraint.rows) == value.shape[0]
  ):
@@ -461,7 +461,7 @@ def tau_equals_inverse_dynamics(
  tau = tau + passive_torque if with_passive_torque else tau
  tau = tau + controller.model.ligament_joint_torque(q, qdot) if with_ligament else tau
 
- if controller.get_nlp.external_forces:
+ if controller.nlp.external_forces:
  raise NotImplementedError(
  "This implicit constraint tau_equals_inverse_dynamics is not implemented yet with external forces"
  )
@@ -554,7 +554,7 @@ def tau_from_muscle_equal_inverse_dynamics(
  muscle_tau = muscle_tau + controller.model.ligament_joint_torque(q, qdot) if with_ligament else muscle_tau
  qddot = controller.states["qddot"].mx if "qddot" in controller.states else controller.controls["qddot"].mx
 
- if controller.get_nlp.external_forces:
+ if controller.nlp.external_forces:
  raise NotImplementedError(
  "This implicit constraint tau_from_muscle_equal_inverse_dynamics is not implemented yet with external forces"
  )
@@ -591,7 +591,7 @@ def implicit_soft_contact_forces(_: Constraint, controller: PenaltyController, *
  force_idx.append(4 + (6 * i_sc))
  force_idx.append(5 + (6 * i_sc))
 
- soft_contact_all = controller.get_nlp.soft_contact_forces_func(
+ soft_contact_all = controller.nlp.soft_contact_forces_func(
  controller.states.mx, controller.controls.mx, controller.parameters.mx
  )
  soft_contact_force = soft_contact_all[force_idx]
@@ -615,7 +615,7 @@ def stochastic_covariance_matrix_continuity_implicit(
  """
  # TODO: Charbie -> This is only True for x=[q, qdot], u=[tau] (have to think on how to generalize it)
 
- if not controller.get_nlp.is_stochastic:
+ if not controller.nlp.is_stochastic:
  raise RuntimeError("This function is only valid for stochastic problems")
 
  if "cholesky_cov" in controller.stochastic_variables.keys():
@@ -650,7 +650,7 @@ def stochastic_covariance_matrix_continuity_implicit(
  cov_implicit_deffect = cov_next - cov_matrix
 
  penalty.expand = (
- controller.get_nlp.dynamics_type.expand_dynamics
+ controller.nlp.dynamics_type.expand_dynamics
  ) # TODO: Charbie -> should this be always true?
  penalty.explicit_derivative = True
  penalty.multi_thread = True
@@ -667,7 +667,7 @@ def stochastic_df_dx_implicit(
  This function constrains the stochastic matrix A to its actual value which is
  A = df/dx
  """
- if not controller.get_nlp.is_stochastic:
+ if not controller.nlp.is_stochastic:
  raise RuntimeError("This function is only valid for stochastic problems")
 
  dt = controller.tf / controller.ns
@@ -746,10 +746,10 @@ def stochastic_helper_matrix_collocation(
  and G = collocation slope constraints (defects).
  """
 
- if not controller.get_nlp.is_stochastic:
+ if not controller.nlp.is_stochastic:
  raise RuntimeError("This function is only valid for stochastic problems")
 
- polynomial_degree = controller.get_nlp.ode_solver.polynomial_degree
+ polynomial_degree = controller.nlp.ode_solver.polynomial_degree
  Mc, _ = ConstraintFunction.Functions.collocation_jacobians(
  penalty,
  controller,
@@ -790,10 +790,10 @@ def stochastic_covariance_matrix_continuity_collocation(
  """
  # TODO: Charbie -> This is only True for x=[q, qdot], u=[tau] (have to think on how to generalize it)
 
- if not controller.get_nlp.is_stochastic:
+ if not controller.nlp.is_stochastic:
  raise RuntimeError("This function is only valid for stochastic problems")
 
- polynomial_degree = controller.get_nlp.ode_solver.polynomial_degree
+ polynomial_degree = controller.nlp.ode_solver.polynomial_degree
  _, Pf = ConstraintFunction.Functions.collocation_jacobians(
  penalty,
  controller,
@@ -847,7 +847,7 @@ def stochastic_mean_sensory_input_equals_reference(
  controls=controller.controls.cx_start,
  parameters=controller.parameters.cx_start,
  stochastic_variables=controller.stochastic_variables.cx_start,
- nlp=controller.get_nlp,
+ nlp=controller.nlp,
  )
  return sensory_input - ref
 
@@ -1176,18 +1176,18 @@ def _add_penalty_to_pool(self, controller: PenaltyController):
 
  if self.penalty_type == PenaltyType.INTERNAL:
  pool = (
- controller.get_nlp.g_internal
- if controller is not None and controller.get_nlp
+ controller.nlp.g_internal
+ if controller is not None and controller.nlp
  else controller.ocp.g_internal
  )
  elif self.penalty_type == ConstraintType.IMPLICIT:
  pool = (
- controller.get_nlp.g_implicit
- if controller is not None and controller.get_nlp
+ controller.nlp.g_implicit
+ if controller is not None and controller.nlp
  else controller.ocp.g_implicit
  )
  elif self.penalty_type == PenaltyType.USER:
- pool = controller.get_nlp.g if controller is not None and controller.get_nlp else controller.ocp.g
+ pool = controller.nlp.g if controller is not None and controller.nlp else controller.ocp.g
  else:
  raise ValueError(f"Invalid constraint type {self.penalty_type}.")
  pool[self.list_index] = self

bioptim/limits/multinode_penalty.py

@@ -84,7 +84,7 @@ def _add_penalty_to_pool(self, controller: list[PenaltyController, PenaltyContro
  )
 
  ocp = controller[0].ocp
- nlp = controller[0].get_nlp
+ nlp = controller[0].nlp
  pool = self._get_pool_to_add_penalty(ocp, nlp)
  pool[self.list_index] = self
 
@@ -378,7 +378,7 @@ def stochastic_helper_matrix_explicit(
  controllers: list[PenaltyController, PenaltyController]
  """
 
- if not controllers[0].get_nlp.is_stochastic:
+ if not controllers[0].nlp.is_stochastic:
  raise RuntimeError("This function is only valid for stochastic problems")
  if controllers[0].phase_idx != controllers[1].phase_idx:
  raise RuntimeError("For this constraint to make sens, the two nodes must belong to the same phase.")
@@ -447,7 +447,7 @@ def stochastic_helper_matrix_implicit(
  controllers: list[PenaltyController, PenaltyController]
  The penalty node elements
  """
- if not controllers[0].get_nlp.is_stochastic:
+ if not controllers[0].nlp.is_stochastic:
  raise RuntimeError("This function is only valid for stochastic problems")
  if controllers[0].phase_idx != controllers[1].phase_idx:
  raise RuntimeError("For this constraint to make sens, the two nodes must belong to the same phase.")
@@ -481,7 +481,7 @@ def stochastic_covariance_matrix_continuity_implicit(
  """
  # TODO: Charbie -> This is only True for x=[q, qdot], u=[tau] (have to think on how to generalize it)
 
- if not controllers[0].get_nlp.is_stochastic:
+ if not controllers[0].nlp.is_stochastic:
  raise RuntimeError("This function is only valid for stochastic problems")
 
  cov_matrix = StochasticBioModel.reshape_to_matrix(
@@ -522,7 +522,7 @@ def stochastic_df_dw_implicit(
  """
  # TODO: Charbie -> This is only True for x=[q, qdot], u=[tau] (have to think on how to generalize it)
 
- if not controllers[0].get_nlp.is_stochastic:
+ if not controllers[0].nlp.is_stochastic:
  raise RuntimeError("This function is only valid for stochastic problems")
 
  dt = controllers[0].tf / controllers[0].ns

bioptim/limits/objective_functions.py

@@ -83,12 +83,12 @@ def _add_penalty_to_pool(self, controller: PenaltyController):
 
  if self.penalty_type == PenaltyType.INTERNAL:
  pool = (
- controller.get_nlp.J_internal
- if controller is not None and controller.get_nlp
+ controller.nlp.J_internal
+ if controller is not None and controller.nlp
  else controller.ocp.J_internal
  )
  elif self.penalty_type == PenaltyType.USER:
- pool = controller.get_nlp.J if controller is not None and controller.get_nlp else controller.ocp.J
+ pool = controller.nlp.J if controller is not None and controller.nlp else controller.ocp.J
  else:
  raise ValueError(f"Invalid objective type {self.penalty_type}.")
  pool[self.list_index] = self
@@ -492,12 +492,12 @@ def _add_penalty_to_pool(self, controller: PenaltyController):
 
  if self.penalty_type == PenaltyType.INTERNAL:
  pool = (
- controller.get_nlp.J_internal
- if controller is not None and controller.get_nlp
+ controller.nlp.J_internal
+ if controller is not None and controller.nlp
  else controller.ocp.J_internal
  )
  elif self.penalty_type == PenaltyType.USER:
- pool = controller.get_nlp.J if controller is not None and controller.get_nlp else controller.ocp.J
+ pool = controller.nlp.J if controller is not None and controller.nlp else controller.ocp.J
  else:
  raise ValueError(f"Invalid objective type {self.penalty_type}.")
  pool[self.list_index] = self

bioptim/limits/penalty.py

@@ -87,12 +87,12 @@ def minimize_controls(penalty: PenaltyOption, controller: PenaltyController, key
  """
 
  penalty.quadratic = True if penalty.quadratic is None else penalty.quadratic
- if key in controller.get_nlp.variable_mappings:
- target_mapping = controller.get_nlp.variable_mappings[key]
+ if key in controller.nlp.variable_mappings:
+ target_mapping = controller.nlp.variable_mappings[key]
  else:
  target_mapping = BiMapping(
- to_first=list(range(controller.get_nlp.controls[key].cx_start.shape[0])),
- to_second=list(range(controller.get_nlp.controls[key].cx_start.shape[0])),
+ to_first=list(range(controller.nlp.controls[key].cx_start.shape[0])),
+ to_second=list(range(controller.nlp.controls[key].cx_start.shape[0])),
  ) # TODO: why if condition, target_mapping not used (Pariterre?)
 
  if penalty.integration_rule == QuadratureRule.RECTANGLE_LEFT:
@@ -159,12 +159,12 @@ def stochastic_minimize_variables(penalty: PenaltyOption, controller: PenaltyCon
  penalty.quadratic = True if penalty.quadratic is None else penalty.quadratic
  penalty.multi_thread = True if penalty.multi_thread is None else penalty.multi_thread
 
- if key in controller.get_nlp.variable_mappings:
- target_mapping = controller.get_nlp.variable_mappings[key]
+ if key in controller.nlp.variable_mappings:
+ target_mapping = controller.nlp.variable_mappings[key]
  else:
  target_mapping = BiMapping(
- to_first=list(range(controller.get_nlp.controls[key].cx_start.shape[0])),
- to_second=list(range(controller.get_nlp.controls[key].cx_start.shape[0])),
+ to_first=list(range(controller.nlp.controls[key].cx_start.shape[0])),
+ to_second=list(range(controller.nlp.controls[key].cx_start.shape[0])),
  )
 
  return controller.stochastic_variables[key].cx_start
@@ -223,7 +223,7 @@ def stochastic_minimize_expected_feedback_efforts(penalty: PenaltyOption, contro
  controls=controller.controls.cx_start,
  parameters=controller.parameters.cx_start,
  stochastic_variables=controller.stochastic_variables.cx_start,
- nlp=controller.get_nlp,
+ nlp=controller.nlp,
  )
 
  e_fb = k_matrix @ ((ee - ref) + controller.model.sensory_noise_magnitude)
@@ -805,13 +805,13 @@ def minimize_contact_forces(
  penalty.cols should not be defined if contact_index is defined
  """
 
- if controller.get_nlp.contact_forces_func is None:
+ if controller.nlp.contact_forces_func is None:
  raise RuntimeError("minimize_contact_forces requires a contact dynamics")
 
  PenaltyFunctionAbstract.set_axes_rows(penalty, contact_index)
  penalty.quadratic = True if penalty.quadratic is None else penalty.quadratic
 
- contact_force = controller.get_nlp.contact_forces_func(
+ contact_force = controller.nlp.contact_forces_func(
  controller.time.cx,
  controller.states.cx_start,
  controller.controls.cx_start,
@@ -840,7 +840,7 @@ def minimize_soft_contact_forces(
  penalty.cols should not be defined if contact_index is defined
  """
 
- if controller.get_nlp.soft_contact_forces_func is None:
+ if controller.nlp.soft_contact_forces_func is None:
  raise RuntimeError("minimize_contact_forces requires a soft contact dynamics")
 
  PenaltyFunctionAbstract.set_axes_rows(penalty, contact_index)
@@ -851,7 +851,7 @@ def minimize_soft_contact_forces(
  force_idx.append(3 + (6 * i_sc))
  force_idx.append(4 + (6 * i_sc))
  force_idx.append(5 + (6 * i_sc))
- soft_contact_force = controller.get_nlp.soft_contact_forces_func(
+ soft_contact_force = controller.nlp.soft_contact_forces_func(
  controller.time.cx, controller.states.cx_start, controller.controls.cx_start, controller.parameters.cx
  )
  return soft_contact_force[force_idx]
@@ -1127,19 +1127,19 @@ def state_continuity(penalty: PenaltyOption, controller: PenaltyController | lis
  if isinstance(penalty.node, (list, tuple)) and len(penalty.node) != 1:
  raise RuntimeError("continuity should be called one node at a time")
 
- penalty.expand = controller.get_nlp.dynamics_type.expand_continuity
+ penalty.expand = controller.nlp.dynamics_type.expand_continuity
 
  if (
  len(penalty.node_idx) > 1
- and not controller.get_nlp.phase_dynamics == PhaseDynamics.SHARED_DURING_THE_PHASE
+ and not controller.nlp.phase_dynamics == PhaseDynamics.SHARED_DURING_THE_PHASE
  ):
  raise NotImplementedError(
  f"Length of node index superior to 1 is not implemented yet,"
  f" actual length {len(penalty.node_idx[0])} "
  )
 
  continuity = controller.states.cx_end
- if controller.get_nlp.ode_solver.is_direct_collocation:
+ if controller.nlp.ode_solver.is_direct_collocation:
  cx = horzcat(*([controller.states.cx_start] + controller.states.cx_intermediates_list))
  continuity -= controller.integrate(
  x0=cx, u=u, p=controller.parameters.cx, s=controller.stochastic_variables.cx_start

bioptim/limits/penalty_controller.py

@@ -81,7 +81,7 @@ def ocp(self):
  return self._ocp
 
  @property
- def get_nlp(self):
+ def nlp(self):
  """
  This method returns the underlying nlp. Please note that acting directly with the nlp is not want you should do.
  Unless you see no way to access what you need otherwise, we strongly suggest that you use the normal path