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#746 Issue Fixed [get_nlp in controller could be renamed to nlp ] #802

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#746 Issue Fixed [get_nlp in controller could be renamed to nlp ] #802

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587d678
#746 Issue Fixed
Nov 9, 2023
842dc41
fix: #706 Issue
Nov 10, 2023
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4 changes: 2 additions & 2 deletions bioptim/examples/stochastic_optimal_control/arm_reaching_muscle_driven.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down Expand Up @@ -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,
)
Expand Down
2 changes: 1 addition & 1 deletion bioptim/examples/stochastic_optimal_control/arm_reaching_torque_driven_explicit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down
12 changes: 6 additions & 6 deletions bioptim/interfaces/solver_options.py
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Original file line number Diff line number Diff line change
Expand Up @@ -484,7 +484,7 @@ def lbfgs_memory(self):
return self._lbfgs_memory

@property
def maximum_iterations(self):
def max_iter(self):
return self._max_iter

@property
Expand Down Expand Up @@ -638,7 +638,7 @@ class ACADOS(Generic):
QP solver to be used in the NLP solver. String in (‘PARTIAL_CONDENSING_HPIPM’, ‘FULL_CONDENSING_QPOASES’,
‘FULL_CONDENSING_HPIPM’, ‘PARTIAL_CONDENSING_QPDUNES’, ‘PARTIAL_CONDENSING_OSQP’).
Default: ‘PARTIAL_CONDENSING_HPIPM’
_hessian_approx: str
_hessian_approximation: str
Hessian approximation.
_integrator_type: str
Integrator type.
Expand Down Expand Up @@ -682,7 +682,7 @@ class ACADOS(Generic):

type: SolverType = SolverType.ACADOS
_qp_solver: str = "PARTIAL_CONDENSING_HPIPM" # FULL_CONDENSING_QPOASES
_hessian_approx: str = "GAUSS_NEWTON"
_hessian_approximation: str = "GAUSS_NEWTON"
_integrator_type: str = "IRK"
_nlp_solver_type: str = "SQP"
_nlp_solver_tol_comp: float = 1e-06
Expand Down Expand Up @@ -722,11 +722,11 @@ def set_option_unsafe(self, val: float | int | str, name: str):
self.set_only_first_options_has_changed(True)

@property
def hessian_approx(self):
return self._hessian_approx
def hessian_approximation(self):
return self._hessian_approximation

def set_hessian_approx(self, val: str):
self._hessian_approx = val
self._hessian_approximation = val
self.set_only_first_options_has_changed(True)

@property
Expand Down
46 changes: 23 additions & 23 deletions bioptim/limits/constraints.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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]
):
Expand Down Expand Up @@ -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"
)
Expand Down Expand Up @@ -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"
)
Expand Down Expand Up @@ -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]
Expand All @@ -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():
Expand Down Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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

Expand Down Expand Up @@ -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
Expand Down
10 changes: 5 additions & 5 deletions bioptim/limits/multinode_penalty.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand Down Expand Up @@ -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.")
Expand Down Expand Up @@ -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.")
Expand Down Expand Up @@ -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(
Expand Down Expand Up @@ -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
Expand Down
12 changes: 6 additions & 6 deletions bioptim/limits/objective_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down
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