#
#     This file is part of CasADi.
#
#     CasADi -- A symbolic framework for dynamic optimization.
#     Copyright (C) 2010-2023 Joel Andersson, Joris Gillis, Moritz Diehl,
#                             KU Leuven. All rights reserved.
#     Copyright (C) 2011-2014 Greg Horn
#
#     CasADi is free software; you can redistribute it and/or
#     modify it under the terms of the GNU Lesser General Public
#     License as published by the Free Software Foundation; either
#     version 3 of the License, or (at your option) any later version.
#
#     CasADi is distributed in the hope that it will be useful,
#     but WITHOUT ANY WARRANTY; without even the implied warranty of
#     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
#     Lesser General Public License for more details.
#
#     You should have received a copy of the GNU Lesser General Public
#     License along with CasADi; if not, write to the Free Software
#     Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
#

# Code generation
# ======================
from casadi import *

# Let's build a trivial symbolic SX graph

x = SX.sym("x")
y = SX.sym("y")
z = x*y+2*y
z += 4*z

# A Function is needed to inspect the graph

f = Function("f", [x,y],[z])

# The default representation is just the name of the function

print(f.__repr__())

# A print statement will call __str__()
# The result will look like a node-by-node tree evaluation

print(f)

# The generate method will insert this node-by-node evaluation in exported C code

f.generate("f_generated")

# This is how the exported code looks like:

print(open('f_generated.c').read())