You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
403 lines
20 KiB
403 lines
20 KiB
4 years ago
|
import sys
|
||
|
|
import os
|
||
|
|
import json
|
||
|
|
import numpy as np
|
||
|
|
from datetime import datetime
|
||
|
|
|
||
|
|
from ctypes import POINTER, CDLL, c_void_p, c_int, cast, c_double, c_char_p
|
||
|
|
|
||
|
|
from copy import deepcopy
|
||
|
|
|
||
|
|
from .generate_c_code_explicit_ode import generate_c_code_explicit_ode
|
||
|
|
from .generate_c_code_implicit_ode import generate_c_code_implicit_ode
|
||
|
|
from .generate_c_code_gnsf import generate_c_code_gnsf
|
||
|
|
from .generate_c_code_discrete_dynamics import generate_c_code_discrete_dynamics
|
||
|
|
from .generate_c_code_constraint import generate_c_code_constraint
|
||
|
|
from .generate_c_code_nls_cost import generate_c_code_nls_cost
|
||
|
|
from .generate_c_code_external_cost import generate_c_code_external_cost
|
||
|
|
from .acados_ocp import AcadosOcp
|
||
|
|
from .acados_model import acados_model_strip_casadi_symbolics
|
||
|
|
from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\
|
||
|
|
format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\
|
||
|
|
set_up_imported_gnsf_model, get_acados_path
|
||
|
|
|
||
|
|
|
||
|
|
class AcadosOcpSolverFast:
|
||
|
|
dlclose = CDLL(None).dlclose
|
||
|
|
dlclose.argtypes = [c_void_p]
|
||
|
|
|
||
|
|
def __init__(self, model_name, N, code_export_dir):
|
||
|
|
|
||
|
|
self.solver_created = False
|
||
|
|
self.N = N
|
||
|
|
self.model_name = model_name
|
||
|
|
|
||
|
|
self.shared_lib_name = f'{code_export_dir}/libacados_ocp_solver_{model_name}.so'
|
||
|
|
|
||
|
|
# get shared_lib
|
||
|
|
self.shared_lib = CDLL(self.shared_lib_name)
|
||
|
|
|
||
|
|
# create capsule
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_create_capsule").restype = c_void_p
|
||
|
|
self.capsule = getattr(self.shared_lib, f"{model_name}_acados_create_capsule")()
|
||
|
|
|
||
|
|
# create solver
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_create").argtypes = [c_void_p]
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_create").restype = c_int
|
||
|
|
assert getattr(self.shared_lib, f"{model_name}_acados_create")(self.capsule)==0
|
||
|
|
self.solver_created = True
|
||
|
|
|
||
|
|
# get pointers solver
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_opts").argtypes = [c_void_p]
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_opts").restype = c_void_p
|
||
|
|
self.nlp_opts = getattr(self.shared_lib, f"{model_name}_acados_get_nlp_opts")(self.capsule)
|
||
|
|
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_dims").argtypes = [c_void_p]
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_dims").restype = c_void_p
|
||
|
|
self.nlp_dims = getattr(self.shared_lib, f"{model_name}_acados_get_nlp_dims")(self.capsule)
|
||
|
|
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_config").argtypes = [c_void_p]
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_config").restype = c_void_p
|
||
|
|
self.nlp_config = getattr(self.shared_lib, f"{model_name}_acados_get_nlp_config")(self.capsule)
|
||
|
|
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_out").argtypes = [c_void_p]
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_out").restype = c_void_p
|
||
|
|
self.nlp_out = getattr(self.shared_lib, f"{model_name}_acados_get_nlp_out")(self.capsule)
|
||
|
|
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_in").argtypes = [c_void_p]
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_in").restype = c_void_p
|
||
|
|
self.nlp_in = getattr(self.shared_lib, f"{model_name}_acados_get_nlp_in")(self.capsule)
|
||
|
|
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_solver").argtypes = [c_void_p]
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_get_nlp_solver").restype = c_void_p
|
||
|
|
self.nlp_solver = getattr(self.shared_lib, f"{model_name}_acados_get_nlp_solver")(self.capsule)
|
||
|
|
|
||
|
|
|
||
|
|
def solve(self):
|
||
|
|
"""
|
||
|
|
Solve the ocp with current input.
|
||
|
|
"""
|
||
|
|
model_name = self.model_name
|
||
|
|
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_solve").argtypes = [c_void_p]
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_solve").restype = c_int
|
||
|
|
status = getattr(self.shared_lib, f"{model_name}_acados_solve")(self.capsule)
|
||
|
|
return status
|
||
|
|
|
||
|
|
def cost_set(self, start_stage_, field_, value_, api='warn'):
|
||
|
|
self.cost_set_slice(start_stage_, start_stage_+1, field_, value_[None], api='warn')
|
||
|
|
return
|
||
|
|
|
||
|
|
def cost_set_slice(self, start_stage_, end_stage_, field_, value_, api='warn'):
|
||
|
|
"""
|
||
|
|
Set numerical data in the cost module of the solver.
|
||
|
|
|
||
|
|
:param stage: integer corresponding to shooting node
|
||
|
|
:param field: string, e.g. 'yref', 'W', 'ext_cost_num_hess'
|
||
|
|
:param value: of appropriate size
|
||
|
|
"""
|
||
|
|
# cast value_ to avoid conversion issues
|
||
|
|
if isinstance(value_, (float, int)):
|
||
|
|
value_ = np.array([value_])
|
||
|
|
value_ = np.ascontiguousarray(np.copy(value_), dtype=np.float64)
|
||
|
|
field = field_
|
||
|
|
field = field.encode('utf-8')
|
||
|
|
dim = np.product(value_.shape[1:])
|
||
|
|
|
||
|
|
start_stage = c_int(start_stage_)
|
||
|
|
end_stage = c_int(end_stage_)
|
||
|
|
self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]
|
||
|
|
self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int
|
||
|
|
|
||
|
|
dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)
|
||
|
|
dims_data = cast(dims.ctypes.data, POINTER(c_int))
|
||
|
|
|
||
|
|
self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config,
|
||
|
|
self.nlp_dims, self.nlp_out, start_stage_, field, dims_data)
|
||
|
|
|
||
|
|
value_shape = value_.shape
|
||
|
|
expected_shape = tuple(np.concatenate([np.array([end_stage_ - start_stage_]), dims]))
|
||
|
|
if len(value_shape) == 2:
|
||
|
|
value_shape = (value_shape[0], value_shape[1], 0)
|
||
|
|
|
||
|
|
elif len(value_shape) == 3:
|
||
|
|
if api=='old':
|
||
|
|
pass
|
||
|
|
elif api=='warn':
|
||
|
|
if not np.all(np.ravel(value_, order='F')==np.ravel(value_, order='K')):
|
||
|
|
raise Exception("Ambiguity in API detected.\n"
|
||
|
|
"Are you making an acados model from scrach? Add api='new' to cost_set and carry on.\n"
|
||
|
|
"Are you seeing this error suddenly in previously running code? Read on.\n"
|
||
|
|
" You are relying on a now-fixed bug in cost_set for field '{}'.\n".format(field_) +
|
||
|
|
" acados_template now correctly passes on any matrices to acados in column major format.\n" +
|
||
|
|
" Two options to fix this error: \n" +
|
||
|
|
" * Add api='old' to cost_set to restore old incorrect behaviour\n" +
|
||
|
|
" * Add api='new' to cost_set and remove any unnatural manipulation of the value argument " +
|
||
|
|
"such as non-mathematical transposes, reshaping, casting to fortran order, etc... " +
|
||
|
|
"If there is no such manipulation, then you have probably been getting an incorrect solution before.")
|
||
|
|
# Get elements in column major order
|
||
|
|
value_ = np.ravel(value_, order='F')
|
||
|
|
elif api=='new':
|
||
|
|
# Get elements in column major order
|
||
|
|
value_ = np.ravel(value_, order='F')
|
||
|
|
else:
|
||
|
|
raise Exception("Unknown api: '{}'".format(api))
|
||
|
|
|
||
|
|
if value_shape != expected_shape:
|
||
|
|
raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension',
|
||
|
|
' for field "{}" with dimension {} (you have {})'.format(
|
||
|
|
field_, expected_shape, value_shape))
|
||
|
|
|
||
|
|
|
||
|
|
value_data = cast(value_.ctypes.data, POINTER(c_double))
|
||
|
|
value_data_p = cast((value_data), c_void_p)
|
||
|
|
|
||
|
|
self.shared_lib.ocp_nlp_cost_model_set_slice.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_int, c_char_p, c_void_p, c_int]
|
||
|
|
self.shared_lib.ocp_nlp_cost_model_set_slice(self.nlp_config,
|
||
|
|
self.nlp_dims, self.nlp_in, start_stage, end_stage, field, value_data_p, dim)
|
||
|
|
return
|
||
|
|
|
||
|
|
def constraints_set(self, start_stage_, field_, value_, api='warn'):
|
||
|
|
self.constraints_set_slice(start_stage_, start_stage_+1, field_, value_[None], api='warn')
|
||
|
|
return
|
||
|
|
|
||
|
|
def constraints_set_slice(self, start_stage_, end_stage_, field_, value_, api='warn'):
|
||
|
|
"""
|
||
|
|
Set numerical data in the constraint module of the solver.
|
||
|
|
|
||
|
|
:param stage: integer corresponding to shooting node
|
||
|
|
:param field: string in ['lbx', 'ubx', 'lbu', 'ubu', 'lg', 'ug', 'lh', 'uh', 'uphi']
|
||
|
|
:param value: of appropriate size
|
||
|
|
"""
|
||
|
|
# cast value_ to avoid conversion issues
|
||
|
|
if isinstance(value_, (float, int)):
|
||
|
|
value_ = np.array([value_])
|
||
|
|
value_ = value_.astype(float)
|
||
|
|
|
||
|
|
field = field_
|
||
|
|
field = field.encode('utf-8')
|
||
|
|
dim = np.product(value_.shape[1:])
|
||
|
|
|
||
|
|
start_stage = c_int(start_stage_)
|
||
|
|
end_stage = c_int(end_stage_)
|
||
|
|
self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]
|
||
|
|
self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int
|
||
|
|
|
||
|
|
dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)
|
||
|
|
dims_data = cast(dims.ctypes.data, POINTER(c_int))
|
||
|
|
|
||
|
|
self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \
|
||
|
|
self.nlp_dims, self.nlp_out, start_stage_, field, dims_data)
|
||
|
|
|
||
|
|
value_shape = value_.shape
|
||
|
|
expected_shape = tuple(np.concatenate([np.array([end_stage_ - start_stage_]), dims]))
|
||
|
|
if len(value_shape) == 2:
|
||
|
|
value_shape = (value_shape[0], value_shape[1], 0)
|
||
|
|
elif len(value_shape) == 3:
|
||
|
|
if api=='old':
|
||
|
|
pass
|
||
|
|
elif api=='warn':
|
||
|
|
if not np.all(np.ravel(value_, order='F')==np.ravel(value_, order='K')):
|
||
|
|
raise Exception("Ambiguity in API detected.\n"
|
||
|
|
"Are you making an acados model from scrach? Add api='new' to constraints_set and carry on.\n"
|
||
|
|
"Are you seeing this error suddenly in previously running code? Read on.\n"
|
||
|
|
" You are relying on a now-fixed bug in constraints_set for field '{}'.\n".format(field_) +
|
||
|
|
" acados_template now correctly passes on any matrices to acados in column major format.\n" +
|
||
|
|
" Two options to fix this error: \n" +
|
||
|
|
" * Add api='old' to constraints_set to restore old incorrect behaviour\n" +
|
||
|
|
" * Add api='new' to constraints_set and remove any unnatural manipulation of the value argument " +
|
||
|
|
"such as non-mathematical transposes, reshaping, casting to fortran order, etc... " +
|
||
|
|
"If there is no such manipulation, then you have probably been getting an incorrect solution before.")
|
||
|
|
# Get elements in column major order
|
||
|
|
value_ = np.ravel(value_, order='F')
|
||
|
|
elif api=='new':
|
||
|
|
# Get elements in column major order
|
||
|
|
value_ = np.ravel(value_, order='F')
|
||
|
|
else:
|
||
|
|
raise Exception("Unknown api: '{}'".format(api))
|
||
|
|
if value_shape != expected_shape:
|
||
|
|
raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \
|
||
|
|
' for field "{}" with dimension {} (you have {})'.format(field_, expected_shape, value_shape))
|
||
|
|
|
||
|
|
value_data = cast(value_.ctypes.data, POINTER(c_double))
|
||
|
|
value_data_p = cast((value_data), c_void_p)
|
||
|
|
|
||
|
|
self.shared_lib.ocp_nlp_constraints_model_set_slice.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_int, c_char_p, c_void_p, c_int]
|
||
|
|
self.shared_lib.ocp_nlp_constraints_model_set_slice(self.nlp_config, \
|
||
|
|
self.nlp_dims, self.nlp_in, start_stage, end_stage, field, value_data_p, dim)
|
||
|
|
return
|
||
|
|
|
||
|
|
# Note: this function should not be used anymore, better use cost_set, constraints_set
|
||
|
|
def set(self, stage_, field_, value_):
|
||
|
|
"""
|
||
|
|
Set numerical data inside the solver.
|
||
|
|
|
||
|
|
:param stage: integer corresponding to shooting node
|
||
|
|
:param field: string in ['x', 'u', 'pi', 'lam', 't', 'p']
|
||
|
|
|
||
|
|
.. note:: regarding lam, t: \n
|
||
|
|
the inequalities are internally organized in the following order: \n
|
||
|
|
[ lbu lbx lg lh lphi ubu ubx ug uh uphi; \n
|
||
|
|
lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi]
|
||
|
|
|
||
|
|
.. note:: pi: multipliers for dynamics equality constraints \n
|
||
|
|
lam: multipliers for inequalities \n
|
||
|
|
t: slack variables corresponding to evaluation of all inequalities (at the solution) \n
|
||
|
|
sl: slack variables of soft lower inequality constraints \n
|
||
|
|
su: slack variables of soft upper inequality constraints \n
|
||
|
|
"""
|
||
|
|
cost_fields = ['y_ref', 'yref']
|
||
|
|
constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu']
|
||
|
|
out_fields = ['x', 'u', 'pi', 'lam', 't', 'z']
|
||
|
|
mem_fields = ['sl', 'su']
|
||
|
|
|
||
|
|
# cast value_ to avoid conversion issues
|
||
|
|
if isinstance(value_, (float, int)):
|
||
|
|
value_ = np.array([value_])
|
||
|
|
value_ = value_.astype(float)
|
||
|
|
|
||
|
|
model_name = self.model_name
|
||
|
|
|
||
|
|
field = field_
|
||
|
|
field = field.encode('utf-8')
|
||
|
|
|
||
|
|
stage = c_int(stage_)
|
||
|
|
|
||
|
|
# treat parameters separately
|
||
|
|
if field_ == 'p':
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_update_params").argtypes = [c_void_p, c_int, POINTER(c_double)]
|
||
|
|
getattr(self.shared_lib, f"{model_name}_acados_update_params").restype = c_int
|
||
|
|
|
||
|
|
value_data = cast(value_.ctypes.data, POINTER(c_double))
|
||
|
|
|
||
|
|
assert getattr(self.shared_lib, f"{model_name}_acados_update_params")(self.capsule, stage, value_data, value_.shape[0])==0
|
||
|
|
else:
|
||
|
|
if field_ not in constraints_fields + cost_fields + out_fields + mem_fields:
|
||
|
|
raise Exception("AcadosOcpSolver.set(): {} is not a valid argument.\
|
||
|
|
\nPossible values are {}. Exiting.".format(field, \
|
||
|
|
constraints_fields + cost_fields + out_fields + ['p']))
|
||
|
|
|
||
|
|
self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_char_p]
|
||
|
|
self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int
|
||
|
|
|
||
|
|
dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \
|
||
|
|
self.nlp_dims, self.nlp_out, stage_, field)
|
||
|
|
|
||
|
|
if value_.shape[0] != dims:
|
||
|
|
msg = 'AcadosOcpSolver.set(): mismatching dimension for field "{}" '.format(field_)
|
||
|
|
msg += 'with dimension {} (you have {})'.format(dims, value_.shape)
|
||
|
|
raise Exception(msg)
|
||
|
|
|
||
|
|
value_data = cast(value_.ctypes.data, POINTER(c_double))
|
||
|
|
value_data_p = cast((value_data), c_void_p)
|
||
|
|
|
||
|
|
if field_ in constraints_fields:
|
||
|
|
self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
|
||
|
|
self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \
|
||
|
|
self.nlp_dims, self.nlp_in, stage, field, value_data_p)
|
||
|
|
elif field_ in cost_fields:
|
||
|
|
self.shared_lib.ocp_nlp_cost_model_set.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
|
||
|
|
self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \
|
||
|
|
self.nlp_dims, self.nlp_in, stage, field, value_data_p)
|
||
|
|
elif field_ in out_fields:
|
||
|
|
self.shared_lib.ocp_nlp_out_set.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
|
||
|
|
self.shared_lib.ocp_nlp_out_set(self.nlp_config, \
|
||
|
|
self.nlp_dims, self.nlp_out, stage, field, value_data_p)
|
||
|
|
elif field_ in mem_fields:
|
||
|
|
self.shared_lib.ocp_nlp_set.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_int, c_char_p, c_void_p]
|
||
|
|
self.shared_lib.ocp_nlp_set(self.nlp_config, \
|
||
|
|
self.nlp_solver, stage, field, value_data_p)
|
||
|
|
return
|
||
|
|
|
||
|
|
|
||
|
|
def get_slice(self, start_stage_, end_stage_, field_):
|
||
|
|
"""
|
||
|
|
Get the last solution of the solver:
|
||
|
|
|
||
|
|
:param start_stage: integer corresponding to shooting node that indicates start of slice
|
||
|
|
:param end_stage: integer corresponding to shooting node that indicates end of slice
|
||
|
|
:param field: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',]
|
||
|
|
|
||
|
|
.. note:: regarding lam, t: \n
|
||
|
|
the inequalities are internally organized in the following order: \n
|
||
|
|
[ lbu lbx lg lh lphi ubu ubx ug uh uphi; \n
|
||
|
|
lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi]
|
||
|
|
|
||
|
|
.. note:: pi: multipliers for dynamics equality constraints \n
|
||
|
|
lam: multipliers for inequalities \n
|
||
|
|
t: slack variables corresponding to evaluation of all inequalities (at the solution) \n
|
||
|
|
sl: slack variables of soft lower inequality constraints \n
|
||
|
|
su: slack variables of soft upper inequality constraints \n
|
||
|
|
"""
|
||
|
|
out_fields = ['x', 'u', 'z', 'pi', 'lam', 't']
|
||
|
|
mem_fields = ['sl', 'su']
|
||
|
|
field = field_
|
||
|
|
field = field.encode('utf-8')
|
||
|
|
|
||
|
|
if (field_ not in out_fields + mem_fields):
|
||
|
|
raise Exception('AcadosOcpSolver.get_slice(): {} is an invalid argument.\
|
||
|
|
\n Possible values are {}. Exiting.'.format(field_, out_fields))
|
||
|
|
|
||
|
|
if not isinstance(start_stage_, int):
|
||
|
|
raise Exception('AcadosOcpSolver.get_slice(): stage index must be Integer.')
|
||
|
|
|
||
|
|
if not isinstance(end_stage_, int):
|
||
|
|
raise Exception('AcadosOcpSolver.get_slice(): stage index must be Integer.')
|
||
|
|
|
||
|
|
if start_stage_ >= end_stage_:
|
||
|
|
raise Exception('AcadosOcpSolver.get_slice(): end stage index must be larger than start stage index')
|
||
|
|
|
||
|
|
if start_stage_ < 0 or end_stage_ > self.N + 1:
|
||
|
|
raise Exception('AcadosOcpSolver.get_slice(): stage index must be in [0, N], got: {}.'.format(self.N))
|
||
|
|
self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_char_p]
|
||
|
|
self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int
|
||
|
|
|
||
|
|
dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \
|
||
|
|
self.nlp_dims, self.nlp_out, start_stage_, field)
|
||
|
|
|
||
|
|
out = np.ascontiguousarray(np.zeros((end_stage_ - start_stage_, dims)), dtype=np.float64)
|
||
|
|
out_data = cast(out.ctypes.data, POINTER(c_double))
|
||
|
|
|
||
|
|
if (field_ in out_fields):
|
||
|
|
self.shared_lib.ocp_nlp_out_get_slice.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_int, c_char_p, c_void_p]
|
||
|
|
self.shared_lib.ocp_nlp_out_get_slice(self.nlp_config, \
|
||
|
|
self.nlp_dims, self.nlp_out, start_stage_, end_stage_, field, out_data)
|
||
|
|
elif field_ in mem_fields:
|
||
|
|
self.shared_lib.ocp_nlp_get_at_stage.argtypes = \
|
||
|
|
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
|
||
|
|
self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \
|
||
|
|
self.nlp_dims, self.nlp_solver, start_stage_, end_stage_, field, out_data)
|
||
|
|
|
||
|
|
return out
|
||
|
|
|
||
|
|
def get_cost(self):
|
||
|
|
"""
|
||
|
|
Returns the cost value of the current solution.
|
||
|
|
"""
|
||
|
|
# compute cost internally
|
||
|
|
self.shared_lib.ocp_nlp_eval_cost.argtypes = [c_void_p, c_void_p, c_void_p]
|
||
|
|
self.shared_lib.ocp_nlp_eval_cost(self.nlp_solver, self.nlp_in, self.nlp_out)
|
||
|
|
|
||
|
|
# create output array
|
||
|
|
out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64)
|
||
|
|
out_data = cast(out.ctypes.data, POINTER(c_double))
|
||
|
|
|
||
|
|
# call getter
|
||
|
|
self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p]
|
||
|
|
|
||
|
|
field = "cost_value".encode('utf-8')
|
||
|
|
self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data)
|
||
|
|
|
||
|
|
return out[0]
|