132 lines
4.8 KiB
Python
132 lines
4.8 KiB
Python
#
|
|
# Copyright 2019 Gianluca Frison, Dimitris Kouzoupis, Robin Verschueren,
|
|
# Andrea Zanelli, Niels van Duijkeren, Jonathan Frey, Tommaso Sartor,
|
|
# Branimir Novoselnik, Rien Quirynen, Rezart Qelibari, Dang Doan,
|
|
# Jonas Koenemann, Yutao Chen, Tobias Schöls, Jonas Schlagenhauf, Moritz Diehl
|
|
#
|
|
# This file is part of acados.
|
|
#
|
|
# The 2-Clause BSD License
|
|
#
|
|
# Redistribution and use in source and binary forms, with or without
|
|
# modification, are permitted provided that the following conditions are met:
|
|
#
|
|
# 1. Redistributions of source code must retain the above copyright notice,
|
|
# this list of conditions and the following disclaimer.
|
|
#
|
|
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
|
# this list of conditions and the following disclaimer in the documentation
|
|
# and/or other materials provided with the distribution.
|
|
#
|
|
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
|
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
|
|
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
|
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
|
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
|
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
|
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
|
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
|
# POSSIBILITY OF SUCH DAMAGE.;
|
|
#
|
|
|
|
import os
|
|
from casadi import *
|
|
from .utils import ALLOWED_CASADI_VERSIONS, is_empty, casadi_version_warning
|
|
|
|
def generate_c_code_gnsf( model, opts ):
|
|
|
|
casadi_version = CasadiMeta.version()
|
|
casadi_opts = dict(mex=False, casadi_int='int', casadi_real='double')
|
|
if casadi_version not in (ALLOWED_CASADI_VERSIONS):
|
|
casadi_version_warning(casadi_version)
|
|
|
|
model_name = model.name
|
|
code_export_dir = opts["code_export_directory"]
|
|
|
|
# set up directory
|
|
if not os.path.exists(code_export_dir):
|
|
os.makedirs(code_export_dir)
|
|
|
|
cwd = os.getcwd()
|
|
os.chdir(code_export_dir)
|
|
model_dir = model_name + '_model'
|
|
if not os.path.exists(model_dir):
|
|
os.mkdir(model_dir)
|
|
model_dir_location = os.path.join('.', model_dir)
|
|
os.chdir(model_dir_location)
|
|
|
|
# obtain gnsf dimensions
|
|
get_matrices_fun = model.get_matrices_fun
|
|
phi_fun = model.phi_fun
|
|
|
|
size_gnsf_A = get_matrices_fun.size_out(0)
|
|
gnsf_nx1 = size_gnsf_A[1]
|
|
gnsf_nz1 = size_gnsf_A[0] - size_gnsf_A[1]
|
|
gnsf_nuhat = max(phi_fun.size_in(1))
|
|
gnsf_ny = max(phi_fun.size_in(0))
|
|
gnsf_nout = max(phi_fun.size_out(0))
|
|
|
|
# set up expressions
|
|
# if the model uses MX because of cost/constraints
|
|
# the DAE can be exported as SX -> detect GNSF in Matlab
|
|
# -> evaluated SX GNSF functions with MX.
|
|
u = model.u
|
|
|
|
if isinstance(u, casadi.MX):
|
|
symbol = MX.sym
|
|
else:
|
|
symbol = SX.sym
|
|
|
|
y = symbol("y", gnsf_ny, 1)
|
|
uhat = symbol("uhat", gnsf_nuhat, 1)
|
|
p = model.p
|
|
x1 = symbol("gnsf_x1", gnsf_nx1, 1)
|
|
x1dot = symbol("gnsf_x1dot", gnsf_nx1, 1)
|
|
z1 = symbol("gnsf_z1", gnsf_nz1, 1)
|
|
dummy = symbol("gnsf_dummy", 1, 1)
|
|
empty_var = symbol("gnsf_empty_var", 0, 0)
|
|
|
|
## generate C code
|
|
fun_name = model_name + '_gnsf_phi_fun'
|
|
phi_fun_ = Function(fun_name, [y, uhat, p], [phi_fun(y, uhat, p)])
|
|
phi_fun_.generate(fun_name, casadi_opts)
|
|
|
|
fun_name = model_name + '_gnsf_phi_fun_jac_y'
|
|
phi_fun_jac_y = model.phi_fun_jac_y
|
|
phi_fun_jac_y_ = Function(fun_name, [y, uhat, p], phi_fun_jac_y(y, uhat, p))
|
|
phi_fun_jac_y_.generate(fun_name, casadi_opts)
|
|
|
|
fun_name = model_name + '_gnsf_phi_jac_y_uhat'
|
|
phi_jac_y_uhat = model.phi_jac_y_uhat
|
|
phi_jac_y_uhat_ = Function(fun_name, [y, uhat, p], phi_jac_y_uhat(y, uhat, p))
|
|
phi_jac_y_uhat_.generate(fun_name, casadi_opts)
|
|
|
|
fun_name = model_name + '_gnsf_f_lo_fun_jac_x1k1uz'
|
|
f_lo_fun_jac_x1k1uz = model.f_lo_fun_jac_x1k1uz
|
|
f_lo_fun_jac_x1k1uz_eval = f_lo_fun_jac_x1k1uz(x1, x1dot, z1, u, p)
|
|
|
|
# avoid codegeneration issue
|
|
if not isinstance(f_lo_fun_jac_x1k1uz_eval, tuple) and is_empty(f_lo_fun_jac_x1k1uz_eval):
|
|
f_lo_fun_jac_x1k1uz_eval = [empty_var]
|
|
|
|
f_lo_fun_jac_x1k1uz_ = Function(fun_name, [x1, x1dot, z1, u, p],
|
|
f_lo_fun_jac_x1k1uz_eval)
|
|
f_lo_fun_jac_x1k1uz_.generate(fun_name, casadi_opts)
|
|
|
|
fun_name = model_name + '_gnsf_get_matrices_fun'
|
|
get_matrices_fun_ = Function(fun_name, [dummy], get_matrices_fun(1))
|
|
get_matrices_fun_.generate(fun_name, casadi_opts)
|
|
|
|
# remove fields for json dump
|
|
del model.phi_fun
|
|
del model.phi_fun_jac_y
|
|
del model.phi_jac_y_uhat
|
|
del model.f_lo_fun_jac_x1k1uz
|
|
del model.get_matrices_fun
|
|
|
|
os.chdir(cwd)
|
|
|
|
return
|