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