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#
# 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())
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