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openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 200 supported car makes and models.
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openpilot_comma/third_party/acados/acados_template/casadi_function_generation.py

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acados: update to 0.2.2 (#28821) * use 0.2.2 * Add mac binaries * Replace libqpOASES with symlink on larch64 * Add comment to build script --------- Co-authored-by: Kacper Rączy <gfw.kra@gmail.com>
2 years ago
#
# Copyright (c) The acados authors.
#
# 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
import casadi as ca
from .utils import is_empty, casadi_length
def get_casadi_symbol(x):
if isinstance(x, ca.MX):
return ca.MX.sym
elif isinstance(x, ca.SX):
return ca.SX.sym
else:
raise TypeError("Expected casadi SX or MX.")
################
# Dynamics
################
def generate_c_code_discrete_dynamics( model, opts ):
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
# load model
x = model.x
u = model.u
p = model.p
phi = model.disc_dyn_expr
model_name = model.name
nx = casadi_length(x)
symbol = get_casadi_symbol(x)
# assume nx1 = nx !!!
lam = symbol('lam', nx, 1)
# generate jacobians
ux = ca.vertcat(u,x)
jac_ux = ca.jacobian(phi, ux)
# generate adjoint
adj_ux = ca.jtimes(phi, ux, lam, True)
# generate hessian
hess_ux = ca.jacobian(adj_ux, ux)
# change directory
cwd = os.getcwd()
model_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model_name}_model'))
if not os.path.exists(model_dir):
os.makedirs(model_dir)
os.chdir(model_dir)
# set up & generate ca.Functions
fun_name = model_name + '_dyn_disc_phi_fun'
phi_fun = ca.Function(fun_name, [x, u, p], [phi])
phi_fun.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_dyn_disc_phi_fun_jac'
phi_fun_jac_ut_xt = ca.Function(fun_name, [x, u, p], [phi, jac_ux.T])
phi_fun_jac_ut_xt.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_dyn_disc_phi_fun_jac_hess'
phi_fun_jac_ut_xt_hess = ca.Function(fun_name, [x, u, lam, p], [phi, jac_ux.T, hess_ux])
phi_fun_jac_ut_xt_hess.generate(fun_name, casadi_codegen_opts)
os.chdir(cwd)
return
def generate_c_code_explicit_ode( model, opts ):
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
generate_hess = opts["generate_hess"]
# load model
x = model.x
u = model.u
p = model.p
f_expl = model.f_expl_expr
model_name = model.name
## get model dimensions
nx = x.size()[0]
nu = u.size()[0]
symbol = get_casadi_symbol(x)
## set up functions to be exported
Sx = symbol('Sx', nx, nx)
Sp = symbol('Sp', nx, nu)
lambdaX = symbol('lambdaX', nx, 1)
fun_name = model_name + '_expl_ode_fun'
## Set up functions
expl_ode_fun = ca.Function(fun_name, [x, u, p], [f_expl])
vdeX = ca.jtimes(f_expl,x,Sx)
vdeP = ca.jacobian(f_expl,u) + ca.jtimes(f_expl,x,Sp)
fun_name = model_name + '_expl_vde_forw'
expl_vde_forw = ca.Function(fun_name, [x, Sx, Sp, u, p], [f_expl, vdeX, vdeP])
adj = ca.jtimes(f_expl, ca.vertcat(x, u), lambdaX, True)
fun_name = model_name + '_expl_vde_adj'
expl_vde_adj = ca.Function(fun_name, [x, lambdaX, u, p], [adj])
if generate_hess:
S_forw = ca.vertcat(ca.horzcat(Sx, Sp), ca.horzcat(ca.DM.zeros(nu,nx), ca.DM.eye(nu)))
hess = ca.mtimes(ca.transpose(S_forw),ca.jtimes(adj, ca.vertcat(x,u), S_forw))
hess2 = []
for j in range(nx+nu):
for i in range(j,nx+nu):
hess2 = ca.vertcat(hess2, hess[i,j])
fun_name = model_name + '_expl_ode_hess'
expl_ode_hess = ca.Function(fun_name, [x, Sx, Sp, lambdaX, u, p], [adj, hess2])
# change directory
cwd = os.getcwd()
model_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model_name}_model'))
if not os.path.exists(model_dir):
os.makedirs(model_dir)
os.chdir(model_dir)
# generate C code
fun_name = model_name + '_expl_ode_fun'
expl_ode_fun.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_expl_vde_forw'
expl_vde_forw.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_expl_vde_adj'
expl_vde_adj.generate(fun_name, casadi_codegen_opts)
if generate_hess:
fun_name = model_name + '_expl_ode_hess'
expl_ode_hess.generate(fun_name, casadi_codegen_opts)
os.chdir(cwd)
return
def generate_c_code_implicit_ode( model, opts ):
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
# load model
x = model.x
xdot = model.xdot
u = model.u
z = model.z
p = model.p
f_impl = model.f_impl_expr
model_name = model.name
# get model dimensions
nx = casadi_length(x)
nz = casadi_length(z)
# generate jacobians
jac_x = ca.jacobian(f_impl, x)
jac_xdot = ca.jacobian(f_impl, xdot)
jac_u = ca.jacobian(f_impl, u)
jac_z = ca.jacobian(f_impl, z)
# Set up functions
p = model.p
fun_name = model_name + '_impl_dae_fun'
impl_dae_fun = ca.Function(fun_name, [x, xdot, u, z, p], [f_impl])
fun_name = model_name + '_impl_dae_fun_jac_x_xdot_z'
impl_dae_fun_jac_x_xdot_z = ca.Function(fun_name, [x, xdot, u, z, p], [f_impl, jac_x, jac_xdot, jac_z])
fun_name = model_name + '_impl_dae_fun_jac_x_xdot_u_z'
impl_dae_fun_jac_x_xdot_u_z = ca.Function(fun_name, [x, xdot, u, z, p], [f_impl, jac_x, jac_xdot, jac_u, jac_z])
fun_name = model_name + '_impl_dae_fun_jac_x_xdot_u'
impl_dae_fun_jac_x_xdot_u = ca.Function(fun_name, [x, xdot, u, z, p], [f_impl, jac_x, jac_xdot, jac_u])
fun_name = model_name + '_impl_dae_jac_x_xdot_u_z'
impl_dae_jac_x_xdot_u_z = ca.Function(fun_name, [x, xdot, u, z, p], [jac_x, jac_xdot, jac_u, jac_z])
if opts["generate_hess"]:
x_xdot_z_u = ca.vertcat(x, xdot, z, u)
symbol = get_casadi_symbol(x)
multiplier = symbol('multiplier', nx + nz)
ADJ = ca.jtimes(f_impl, x_xdot_z_u, multiplier, True)
HESS = ca.jacobian(ADJ, x_xdot_z_u)
fun_name = model_name + '_impl_dae_hess'
impl_dae_hess = ca.Function(fun_name, [x, xdot, u, z, multiplier, p], [HESS])
# change directory
cwd = os.getcwd()
model_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model_name}_model'))
if not os.path.exists(model_dir):
os.makedirs(model_dir)
os.chdir(model_dir)
# generate C code
fun_name = model_name + '_impl_dae_fun'
impl_dae_fun.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_impl_dae_fun_jac_x_xdot_z'
impl_dae_fun_jac_x_xdot_z.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_impl_dae_jac_x_xdot_u_z'
impl_dae_jac_x_xdot_u_z.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_impl_dae_fun_jac_x_xdot_u_z'
impl_dae_fun_jac_x_xdot_u_z.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_impl_dae_fun_jac_x_xdot_u'
impl_dae_fun_jac_x_xdot_u.generate(fun_name, casadi_codegen_opts)
if opts["generate_hess"]:
fun_name = model_name + '_impl_dae_hess'
impl_dae_hess.generate(fun_name, casadi_codegen_opts)
os.chdir(cwd)
return
def generate_c_code_gnsf( model, opts ):
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
model_name = model.name
# set up directory
cwd = os.getcwd()
model_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model_name}_model'))
if not os.path.exists(model_dir):
os.makedirs(model_dir)
os.chdir(model_dir)
# 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 ca.MX because of cost/constraints
# the DAE can be exported as ca.SX -> detect GNSF in Matlab
# -> evaluated ca.SX GNSF functions with ca.MX.
u = model.u
symbol = get_casadi_symbol(u)
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_ = ca.Function(fun_name, [y, uhat, p], [phi_fun(y, uhat, p)])
phi_fun_.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_gnsf_phi_fun_jac_y'
phi_fun_jac_y = model.phi_fun_jac_y
phi_fun_jac_y_ = ca.Function(fun_name, [y, uhat, p], phi_fun_jac_y(y, uhat, p))
phi_fun_jac_y_.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_gnsf_phi_jac_y_uhat'
phi_jac_y_uhat = model.phi_jac_y_uhat
phi_jac_y_uhat_ = ca.Function(fun_name, [y, uhat, p], phi_jac_y_uhat(y, uhat, p))
phi_jac_y_uhat_.generate(fun_name, casadi_codegen_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_ = ca.Function(fun_name, [x1, x1dot, z1, u, p],
f_lo_fun_jac_x1k1uz_eval)
f_lo_fun_jac_x1k1uz_.generate(fun_name, casadi_codegen_opts)
fun_name = model_name + '_gnsf_get_matrices_fun'
get_matrices_fun_ = ca.Function(fun_name, [dummy], get_matrices_fun(1))
get_matrices_fun_.generate(fun_name, casadi_codegen_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
################
# Cost
################
def generate_c_code_external_cost(model, stage_type, opts):
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
x = model.x
p = model.p
u = model.u
z = model.z
symbol = get_casadi_symbol(x)
if stage_type == 'terminal':
suffix_name = "_cost_ext_cost_e_fun"
suffix_name_hess = "_cost_ext_cost_e_fun_jac_hess"
suffix_name_jac = "_cost_ext_cost_e_fun_jac"
ext_cost = model.cost_expr_ext_cost_e
custom_hess = model.cost_expr_ext_cost_custom_hess_e
# Last stage cannot depend on u and z
u = symbol("u", 0, 0)
z = symbol("z", 0, 0)
elif stage_type == 'path':
suffix_name = "_cost_ext_cost_fun"
suffix_name_hess = "_cost_ext_cost_fun_jac_hess"
suffix_name_jac = "_cost_ext_cost_fun_jac"
ext_cost = model.cost_expr_ext_cost
custom_hess = model.cost_expr_ext_cost_custom_hess
elif stage_type == 'initial':
suffix_name = "_cost_ext_cost_0_fun"
suffix_name_hess = "_cost_ext_cost_0_fun_jac_hess"
suffix_name_jac = "_cost_ext_cost_0_fun_jac"
ext_cost = model.cost_expr_ext_cost_0
custom_hess = model.cost_expr_ext_cost_custom_hess_0
nunx = x.shape[0] + u.shape[0]
# set up functions to be exported
fun_name = model.name + suffix_name
fun_name_hess = model.name + suffix_name_hess
fun_name_jac = model.name + suffix_name_jac
# generate expression for full gradient and Hessian
hess_uxz, grad_uxz = ca.hessian(ext_cost, ca.vertcat(u, x, z))
hess_ux = hess_uxz[:nunx, :nunx]
hess_z = hess_uxz[nunx:, nunx:]
hess_z_ux = hess_uxz[nunx:, :nunx]
if custom_hess is not None:
hess_ux = custom_hess
ext_cost_fun = ca.Function(fun_name, [x, u, z, p], [ext_cost])
ext_cost_fun_jac_hess = ca.Function(
fun_name_hess, [x, u, z, p], [ext_cost, grad_uxz, hess_ux, hess_z, hess_z_ux]
)
ext_cost_fun_jac = ca.Function(
fun_name_jac, [x, u, z, p], [ext_cost, grad_uxz]
)
# change directory
cwd = os.getcwd()
cost_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model.name}_cost'))
if not os.path.exists(cost_dir):
os.makedirs(cost_dir)
os.chdir(cost_dir)
ext_cost_fun.generate(fun_name, casadi_codegen_opts)
ext_cost_fun_jac_hess.generate(fun_name_hess, casadi_codegen_opts)
ext_cost_fun_jac.generate(fun_name_jac, casadi_codegen_opts)
os.chdir(cwd)
return
def generate_c_code_nls_cost( model, cost_name, stage_type, opts ):
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
x = model.x
z = model.z
p = model.p
u = model.u
symbol = get_casadi_symbol(x)
if stage_type == 'terminal':
middle_name = '_cost_y_e'
u = symbol('u', 0, 0)
y_expr = model.cost_y_expr_e
elif stage_type == 'initial':
middle_name = '_cost_y_0'
y_expr = model.cost_y_expr_0
elif stage_type == 'path':
middle_name = '_cost_y'
y_expr = model.cost_y_expr
# change directory
cwd = os.getcwd()
cost_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model.name}_cost'))
if not os.path.exists(cost_dir):
os.makedirs(cost_dir)
os.chdir(cost_dir)
# set up expressions
cost_jac_expr = ca.transpose(ca.jacobian(y_expr, ca.vertcat(u, x)))
dy_dz = ca.jacobian(y_expr, z)
ny = casadi_length(y_expr)
y = symbol('y', ny, 1)
y_adj = ca.jtimes(y_expr, ca.vertcat(u, x), y, True)
y_hess = ca.jacobian(y_adj, ca.vertcat(u, x))
## generate C code
suffix_name = '_fun'
fun_name = cost_name + middle_name + suffix_name
y_fun = ca.Function( fun_name, [x, u, z, p], [ y_expr ])
y_fun.generate( fun_name, casadi_codegen_opts )
suffix_name = '_fun_jac_ut_xt'
fun_name = cost_name + middle_name + suffix_name
y_fun_jac_ut_xt = ca.Function(fun_name, [x, u, z, p], [ y_expr, cost_jac_expr, dy_dz ])
y_fun_jac_ut_xt.generate( fun_name, casadi_codegen_opts )
suffix_name = '_hess'
fun_name = cost_name + middle_name + suffix_name
y_hess = ca.Function(fun_name, [x, u, z, y, p], [ y_hess ])
y_hess.generate( fun_name, casadi_codegen_opts )
os.chdir(cwd)
return
def generate_c_code_conl_cost(model, cost_name, stage_type, opts):
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
x = model.x
z = model.z
p = model.p
symbol = get_casadi_symbol(x)
if stage_type == 'terminal':
u = symbol('u', 0, 0)
yref = model.cost_r_in_psi_expr_e
inner_expr = model.cost_y_expr_e - yref
outer_expr = model.cost_psi_expr_e
res_expr = model.cost_r_in_psi_expr_e
suffix_name_fun = '_conl_cost_e_fun'
suffix_name_fun_jac_hess = '_conl_cost_e_fun_jac_hess'
custom_hess = model.cost_conl_custom_outer_hess_e
elif stage_type == 'initial':
u = model.u
yref = model.cost_r_in_psi_expr_0
inner_expr = model.cost_y_expr_0 - yref
outer_expr = model.cost_psi_expr_0
res_expr = model.cost_r_in_psi_expr_0
suffix_name_fun = '_conl_cost_0_fun'
suffix_name_fun_jac_hess = '_conl_cost_0_fun_jac_hess'
custom_hess = model.cost_conl_custom_outer_hess_0
elif stage_type == 'path':
u = model.u
yref = model.cost_r_in_psi_expr
inner_expr = model.cost_y_expr - yref
outer_expr = model.cost_psi_expr
res_expr = model.cost_r_in_psi_expr
suffix_name_fun = '_conl_cost_fun'
suffix_name_fun_jac_hess = '_conl_cost_fun_jac_hess'
custom_hess = model.cost_conl_custom_outer_hess
# set up function names
fun_name_cost_fun = model.name + suffix_name_fun
fun_name_cost_fun_jac_hess = model.name + suffix_name_fun_jac_hess
# set up functions to be exported
outer_loss_fun = ca.Function('psi', [res_expr, p], [outer_expr])
cost_expr = outer_loss_fun(inner_expr, p)
outer_loss_grad_fun = ca.Function('outer_loss_grad', [res_expr, p], [ca.jacobian(outer_expr, res_expr).T])
if custom_hess is None:
outer_hess_fun = ca.Function('inner_hess', [res_expr, p], [ca.hessian(outer_loss_fun(res_expr, p), res_expr)[0]])
else:
outer_hess_fun = ca.Function('inner_hess', [res_expr, p], [custom_hess])
Jt_ux_expr = ca.jacobian(inner_expr, ca.vertcat(u, x)).T
Jt_z_expr = ca.jacobian(inner_expr, z).T
cost_fun = ca.Function(
fun_name_cost_fun,
[x, u, z, yref, p],
[cost_expr])
cost_fun_jac_hess = ca.Function(
fun_name_cost_fun_jac_hess,
[x, u, z, yref, p],
[cost_expr, outer_loss_grad_fun(inner_expr, p), Jt_ux_expr, Jt_z_expr, outer_hess_fun(inner_expr, p)]
)
# change directory
cwd = os.getcwd()
cost_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model.name}_cost'))
if not os.path.exists(cost_dir):
os.makedirs(cost_dir)
os.chdir(cost_dir)
# generate C code
cost_fun.generate(fun_name_cost_fun, casadi_codegen_opts)
cost_fun_jac_hess.generate(fun_name_cost_fun_jac_hess, casadi_codegen_opts)
os.chdir(cwd)
return
################
# Constraints
################
def generate_c_code_constraint( model, con_name, is_terminal, opts ):
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
# load constraint variables and expression
x = model.x
p = model.p
symbol = get_casadi_symbol(x)
if is_terminal:
con_h_expr = model.con_h_expr_e
con_phi_expr = model.con_phi_expr_e
# create dummy u, z
u = symbol('u', 0, 0)
z = symbol('z', 0, 0)
else:
con_h_expr = model.con_h_expr
con_phi_expr = model.con_phi_expr
u = model.u
z = model.z
if (not is_empty(con_h_expr)) and (not is_empty(con_phi_expr)):
raise Exception("acados: you can either have constraint_h, or constraint_phi, not both.")
if (is_empty(con_h_expr) and is_empty(con_phi_expr)):
# both empty -> nothing to generate
return
if is_empty(con_h_expr):
constr_type = 'BGP'
else:
constr_type = 'BGH'
if is_empty(p):
p = symbol('p', 0, 0)
if is_empty(z):
z = symbol('z', 0, 0)
if not (is_empty(con_h_expr)) and opts['generate_hess']:
# multipliers for hessian
nh = casadi_length(con_h_expr)
lam_h = symbol('lam_h', nh, 1)
# set up & change directory
cwd = os.getcwd()
constraints_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model.name}_constraints'))
if not os.path.exists(constraints_dir):
os.makedirs(constraints_dir)
os.chdir(constraints_dir)
# export casadi functions
if constr_type == 'BGH':
if is_terminal:
fun_name = con_name + '_constr_h_e_fun_jac_uxt_zt'
else:
fun_name = con_name + '_constr_h_fun_jac_uxt_zt'
jac_ux_t = ca.transpose(ca.jacobian(con_h_expr, ca.vertcat(u,x)))
jac_z_t = ca.jacobian(con_h_expr, z)
constraint_fun_jac_tran = ca.Function(fun_name, [x, u, z, p], \
[con_h_expr, jac_ux_t, jac_z_t])
constraint_fun_jac_tran.generate(fun_name, casadi_codegen_opts)
if opts['generate_hess']:
if is_terminal:
fun_name = con_name + '_constr_h_e_fun_jac_uxt_zt_hess'
else:
fun_name = con_name + '_constr_h_fun_jac_uxt_zt_hess'
# adjoint
adj_ux = ca.jtimes(con_h_expr, ca.vertcat(u, x), lam_h, True)
# hessian
hess_ux = ca.jacobian(adj_ux, ca.vertcat(u, x))
adj_z = ca.jtimes(con_h_expr, z, lam_h, True)
hess_z = ca.jacobian(adj_z, z)
# set up functions
constraint_fun_jac_tran_hess = \
ca.Function(fun_name, [x, u, lam_h, z, p], \
[con_h_expr, jac_ux_t, hess_ux, jac_z_t, hess_z])
# generate C code
constraint_fun_jac_tran_hess.generate(fun_name, casadi_codegen_opts)
if is_terminal:
fun_name = con_name + '_constr_h_e_fun'
else:
fun_name = con_name + '_constr_h_fun'
h_fun = ca.Function(fun_name, [x, u, z, p], [con_h_expr])
h_fun.generate(fun_name, casadi_codegen_opts)
else: # BGP constraint
if is_terminal:
fun_name = con_name + '_phi_e_constraint'
r = model.con_r_in_phi_e
con_r_expr = model.con_r_expr_e
else:
fun_name = con_name + '_phi_constraint'
r = model.con_r_in_phi
con_r_expr = model.con_r_expr
nphi = casadi_length(con_phi_expr)
con_phi_expr_x_u_z = ca.substitute(con_phi_expr, r, con_r_expr)
phi_jac_u = ca.jacobian(con_phi_expr_x_u_z, u)
phi_jac_x = ca.jacobian(con_phi_expr_x_u_z, x)
phi_jac_z = ca.jacobian(con_phi_expr_x_u_z, z)
hess = ca.hessian(con_phi_expr[0], r)[0]
for i in range(1, nphi):
hess = ca.vertcat(hess, ca.hessian(con_phi_expr[i], r)[0])
r_jac_u = ca.jacobian(con_r_expr, u)
r_jac_x = ca.jacobian(con_r_expr, x)
constraint_phi = \
ca.Function(fun_name, [x, u, z, p], \
[con_phi_expr_x_u_z, \
ca.vertcat(ca.transpose(phi_jac_u), ca.transpose(phi_jac_x)), \
ca.transpose(phi_jac_z), \
hess,
ca.vertcat(ca.transpose(r_jac_u), ca.transpose(r_jac_x))])
constraint_phi.generate(fun_name, casadi_codegen_opts)
# change directory back
os.chdir(cwd)
return