def set_up_imported_gnsf_model(acados_formulation):
gnsf = acados_formulation.gnsf_model
# check CasADi version
# dump_casadi_version = gnsf['casadi_version']
# casadi_version = CasadiMeta.version()
# if not casadi_version == dump_casadi_version:
# print("WARNING: GNSF model was dumped with another CasADi version.\n"
# + "This might yield errors. Please use the same version for compatibility, serialize version: "
# + dump_casadi_version + " current Python CasADi verison: " + casadi_version)
# input("Press any key to attempt to continue...")
# load model
phi_fun = Function.deserialize(gnsf['phi_fun'])
phi_fun_jac_y = Function.deserialize(gnsf['phi_fun_jac_y'])
phi_jac_y_uhat = Function.deserialize(gnsf['phi_jac_y_uhat'])
get_matrices_fun = Function.deserialize(gnsf['get_matrices_fun'])
# obtain gnsf dimensions
size_gnsf_A = get_matrices_fun.size_out(0)
acados_formulation.dims.gnsf_nx1 = size_gnsf_A[1]
acados_formulation.dims.gnsf_nz1 = size_gnsf_A[0] - size_gnsf_A[1]
acados_formulation.dims.gnsf_nuhat = max(phi_fun.size_in(1))
acados_formulation.dims.gnsf_ny = max(phi_fun.size_in(0))
acados_formulation.dims.gnsf_nout = max(phi_fun.size_out(0))
# save gnsf functions in model
acados_formulation.model.phi_fun = phi_fun
acados_formulation.model.phi_fun_jac_y = phi_fun_jac_y
acados_formulation.model.phi_jac_y_uhat = phi_jac_y_uhat
acados_formulation.model.get_matrices_fun = get_matrices_fun
# get_matrices_fun = Function([model_name,'_gnsf_get_matrices_fun'], {dummy},...
# {A, B, C, E, L_x, L_xdot, L_z, L_u, A_LO, c, E_LO, B_LO,...
# nontrivial_f_LO, purely_linear, ipiv_x, ipiv_z, c_LO});
get_matrices_out = get_matrices_fun(0)
acados_formulation.model.gnsf['nontrivial_f_LO'] = int(
get_matrices_out[12])
acados_formulation.model.gnsf['purely_linear'] = int(get_matrices_out[13])
if "f_lo_fun_jac_x1k1uz" in gnsf:
f_lo_fun_jac_x1k1uz = Function.deserialize(gnsf['f_lo_fun_jac_x1k1uz'])
acados_formulation.model.f_lo_fun_jac_x1k1uz = f_lo_fun_jac_x1k1uz
else:
dummy_var_x1 = SX.sym('dummy_var_x1', acados_formulation.dims.gnsf_nx1)
dummy_var_x1dot = SX.sym('dummy_var_x1dot',
acados_formulation.dims.gnsf_nx1)
dummy_var_z1 = SX.sym('dummy_var_z1', acados_formulation.dims.gnsf_nz1)
dummy_var_u = SX.sym('dummy_var_z1', acados_formulation.dims.nu)
dummy_var_p = SX.sym('dummy_var_z1', acados_formulation.dims.np)
empty_var = SX.sym('empty_var', 0, 0)
empty_fun = Function('empty_fun', \
[dummy_var_x1, dummy_var_x1dot, dummy_var_z1, dummy_var_u, dummy_var_p],
[empty_var])
acados_formulation.model.f_lo_fun_jac_x1k1uz = empty_fun
del acados_formulation.gnsf_model
def set_up_imported_gnsf_model(acados_formulation):
gnsf = acados_formulation.gnsf_model
# check CasADi version
dump_casadi_version = gnsf['casadi_version']
casadi_version = CasadiMeta.version()
if not casadi_version == dump_casadi_version:
print(
"WARNING: GNSF model was dumped with another CasADi version.\n" +
"This might yield errors. Please use the same version for compatibility, serialize version: "
+ dump_casadi_version + " current Python CasADi verison: " +
casadi_version)
input("Press any key to attempt to continue...")
# load model
phi_fun = Function.deserialize(gnsf['phi_fun'])
phi_fun_jac_y = Function.deserialize(gnsf['phi_fun_jac_y'])
phi_jac_y_uhat = Function.deserialize(gnsf['phi_jac_y_uhat'])
get_matrices_fun = Function.deserialize(gnsf['get_matrices_fun'])
# obtain gnsf dimensions
size_gnsf_A = get_matrices_fun.size_out(0)
acados_formulation.dims.gnsf_nx1 = size_gnsf_A[1]
acados_formulation.dims.gnsf_nz1 = size_gnsf_A[0] - size_gnsf_A[1]
acados_formulation.dims.gnsf_nuhat = max(phi_fun.size_in(1))
acados_formulation.dims.gnsf_ny = max(phi_fun.size_in(0))
acados_formulation.dims.gnsf_nout = max(phi_fun.size_out(0))
# save gnsf functions in model
acados_formulation.model.phi_fun = phi_fun
acados_formulation.model.phi_fun_jac_y = phi_fun_jac_y
acados_formulation.model.phi_jac_y_uhat = phi_jac_y_uhat
acados_formulation.model.get_matrices_fun = get_matrices_fun
if "f_lo_fun_jac_x1k1uz" in gnsf:
f_lo_fun_jac_x1k1uz = Function.deserialize(gnsf['f_lo_fun_jac_x1k1uz'])
acados_formulation.model.f_lo_fun_jac_x1k1uz = f_lo_fun_jac_x1k1uz
else:
dummy_var_x1 = SX.sym('dummy_var_x1', acados_formulation.dims.gnsf_nx1)
dummy_var_x1dot = SX.sym('dummy_var_x1dot',
acados_formulation.dims.gnsf_nx1)
dummy_var_z1 = SX.sym('dummy_var_z1', acados_formulation.dims.gnsf_nz1)
dummy_var_u = SX.sym('dummy_var_z1', acados_formulation.dims.nu)
dummy_var_p = SX.sym('dummy_var_z1', acados_formulation.dims.np)
empty_var = SX.sym('empty_var', 0, 0)
empty_fun = Function('empty_fun', \
[dummy_var_x1, dummy_var_x1dot, dummy_var_z1, dummy_var_u, dummy_var_p],
[empty_var])
acados_formulation.model.f_lo_fun_jac_x1k1uz = empty_fun
del acados_formulation.gnsf_model
def set_up_imported_gnsf_model(acados_ocp):
gnsf = acados_ocp.gnsf_model
# check CasADi version
dump_casadi_version = gnsf['casadi_version']
casadi_version = CasadiMeta.version()
if not casadi_version == dump_casadi_version:
raise Exception("GNSF model was dumped with another CasADi version.\n"
+ "Please use the same version for compatibility, serialize version:"
+ dump_casadi_version + " current Python CasADi verison: " + casadi_version)
# load model
phi_fun = Function.deserialize(gnsf['phi_fun'])
phi_fun_jac_y = Function.deserialize(gnsf['phi_fun_jac_y'])
phi_jac_y_uhat = Function.deserialize(gnsf['phi_jac_y_uhat'])
f_lo_fun_jac_x1k1uz = Function.deserialize(gnsf['f_lo_fun_jac_x1k1uz'])
get_matrices_fun = Function.deserialize(gnsf['get_matrices_fun'])
# obtain gnsf dimensions
size_gnsf_A = get_matrices_fun.size_out(0)
acados_ocp.dims.gnsf_nx1 = size_gnsf_A[1]
acados_ocp.dims.gnsf_nz1 = size_gnsf_A[0] - size_gnsf_A[1]
acados_ocp.dims.gnsf_nuhat = max(phi_fun.size_in(1))
acados_ocp.dims.gnsf_ny = max(phi_fun.size_in(0))
acados_ocp.dims.gnsf_nout = max(phi_fun.size_out(0))
# save gnsf functions in model
acados_ocp.model.phi_fun = phi_fun
acados_ocp.model.phi_fun_jac_y = phi_fun_jac_y
acados_ocp.model.phi_jac_y_uhat = phi_jac_y_uhat
acados_ocp.model.f_lo_fun_jac_x1k1uz = f_lo_fun_jac_x1k1uz
acados_ocp.model.get_matrices_fun = get_matrices_fun
del acados_ocp.gnsf_model