1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
|
#
# 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
#
# Function constructors
# =======================
from casadi import *
x = SX.sym("x") # A scalar (1-by-1 matrix) symbolic primitive
y = SX.sym("y",2) # A vector (n-by-1 matrix) symbolic primitive
z = SX.sym("z",2,3) # An n-by-m matrix symbolic primitive
ins = [x,y] # function inputs
outs = [x,y,vertcat(x,y),y*x,0]
print(outs)
f = Function("f", ins, outs)
# f now has two inputs and a 4 outputs:
print(f.n_in())
print(f.n_out())
# The outputs has the following string representation.
# Note how all elements of out have been converted to SX by
# automatic typecasting functionality
f_out = f(*f.sx_in())
for i in range(3):
print(f_out[i])
|
