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							113 lines
						
					
					
						
							3.8 KiB
						
					
					
				
			
		
		
	
	
							113 lines
						
					
					
						
							3.8 KiB
						
					
					
				| #
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| # Copyright 2019 Gianluca Frison, Dimitris Kouzoupis, Robin Verschueren,
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| # Andrea Zanelli, Niels van Duijkeren, Jonathan Frey, Tommaso Sartor,
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| # Branimir Novoselnik, Rien Quirynen, Rezart Qelibari, Dang Doan,
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| # Jonas Koenemann, Yutao Chen, Tobias Schöls, Jonas Schlagenhauf, Moritz Diehl
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| #
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| # This file is part of acados.
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| #
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| # The 2-Clause BSD License
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| #
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| # Redistribution and use in source and binary forms, with or without
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| # modification, are permitted provided that the following conditions are met:
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| #
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| # 1. Redistributions of source code must retain the above copyright notice,
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| # this list of conditions and the following disclaimer.
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| #
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| # 2. Redistributions in binary form must reproduce the above copyright notice,
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| # this list of conditions and the following disclaimer in the documentation
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| # and/or other materials provided with the distribution.
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| #
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| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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| # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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| # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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| # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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| # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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| # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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| # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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| # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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| # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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| # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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| # POSSIBILITY OF SUCH DAMAGE.;
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| #
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| 
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| import os
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| from casadi import *
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| from .utils import ALLOWED_CASADI_VERSIONS, casadi_length, casadi_version_warning
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| 
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| def generate_c_code_nls_cost( model, cost_name, stage_type, opts ):
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| 
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|     casadi_version = CasadiMeta.version()
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|     casadi_opts = dict(mex=False, casadi_int='int', casadi_real='double')
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| 
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|     if casadi_version not in (ALLOWED_CASADI_VERSIONS):
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|         casadi_version_warning(casadi_version)
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| 
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|     x = model.x
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|     p = model.p
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| 
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|     if isinstance(x, casadi.MX):
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|         symbol = MX.sym
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|     else:
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|         symbol = SX.sym
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| 
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|     if stage_type == 'terminal':
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|         middle_name = '_cost_y_e'
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|         u = symbol('u', 0, 0)
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|         cost_expr = model.cost_y_expr_e
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| 
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|     elif stage_type == 'initial':
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|         middle_name = '_cost_y_0'
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|         u = model.u
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|         cost_expr = model.cost_y_expr_0
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| 
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|     elif stage_type == 'path':
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|         middle_name = '_cost_y'
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|         u = model.u
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|         cost_expr = model.cost_y_expr
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| 
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|     # set up directory
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|     code_export_dir = opts["code_export_directory"]
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|     if not os.path.exists(code_export_dir):
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|         os.makedirs(code_export_dir)
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| 
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|     cwd = os.getcwd()
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|     os.chdir(code_export_dir)
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|     gen_dir = cost_name + '_cost'
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|     if not os.path.exists(gen_dir):
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|         os.mkdir(gen_dir)
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|     gen_dir_location = './' + gen_dir
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|     os.chdir(gen_dir_location)
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| 
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|     # set up expressions
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|     cost_jac_expr = transpose(jacobian(cost_expr, vertcat(u, x)))
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| 
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|     ny = casadi_length(cost_expr)
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| 
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|     y = symbol('y', ny, 1)
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| 
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|     y_adj = jtimes(cost_expr, vertcat(u, x), y, True)
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|     y_hess = jacobian(y_adj, vertcat(u, x))
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| 
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|     ## generate C code
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|     suffix_name = '_fun'
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|     fun_name = cost_name + middle_name + suffix_name
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|     y_fun = Function( fun_name, [x, u, p], \
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|             [ cost_expr ])
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|     y_fun.generate( fun_name, casadi_opts )
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| 
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|     suffix_name = '_fun_jac_ut_xt'
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|     fun_name = cost_name + middle_name + suffix_name
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|     y_fun_jac_ut_xt = Function(fun_name, [x, u, p], \
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|             [ cost_expr, cost_jac_expr ])
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|     y_fun_jac_ut_xt.generate( fun_name, casadi_opts )
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| 
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|     suffix_name = '_hess'
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|     fun_name = cost_name + middle_name + suffix_name
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|     y_hess = Function(fun_name, [x, u, y, p], [ y_hess ])
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|     y_hess.generate( fun_name, casadi_opts )
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| 
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|     os.chdir(cwd)
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| 
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|     return
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| 
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| 
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