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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
#
from casadi import *
a = SX.sym("a")
b = SX.sym("b")
c = a+b
c = c.printme(13)
d = c**2
print(d)
f = Function("f", [a,b],[d])
# When the graph is evaluated, a printout of c will occur (if you have set WITH_PRINTME to ON in CMakeCache.txt)
# Printout reads '|> 13: 7'
# 13 is an identifier of choice, 7 is the numerical value of c
f(4,3)
dd_da = jacobian(d, a)
J = Function('J', [a,b], [dd_da])
# The first derivative still depends on c
# Printout reads '|> 13: 11'
J(2,9)
d2d_da2 = jacobian(dd_da, a)
J = Function('J', [a,b], [d2d_da2])
# second derivative doesn't, so we don't get a printout
J(2,9)
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