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132 lines
4.8 KiB
132 lines
4.8 KiB
4 years ago
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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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import os
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from casadi import *
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from .utils import ALLOWED_CASADI_VERSIONS, is_empty, casadi_version_warning
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def generate_c_code_gnsf( model, opts ):
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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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if casadi_version not in (ALLOWED_CASADI_VERSIONS):
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casadi_version_warning(casadi_version)
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model_name = model.name
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code_export_dir = opts["code_export_directory"]
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# set up 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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cwd = os.getcwd()
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os.chdir(code_export_dir)
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model_dir = model_name + '_model'
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if not os.path.exists(model_dir):
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os.mkdir(model_dir)
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model_dir_location = './' + model_dir
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os.chdir(model_dir_location)
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# obtain gnsf dimensions
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get_matrices_fun = model.get_matrices_fun
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phi_fun = model.phi_fun
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size_gnsf_A = get_matrices_fun.size_out(0)
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gnsf_nx1 = size_gnsf_A[1]
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gnsf_nz1 = size_gnsf_A[0] - size_gnsf_A[1]
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gnsf_nuhat = max(phi_fun.size_in(1))
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gnsf_ny = max(phi_fun.size_in(0))
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gnsf_nout = max(phi_fun.size_out(0))
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# set up expressions
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# if the model uses MX because of cost/constraints
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# the DAE can be exported as SX -> detect GNSF in Matlab
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# -> evaluated SX GNSF functions with MX.
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u = model.u
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if isinstance(u, 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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y = symbol("y", gnsf_ny, 1)
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uhat = symbol("uhat", gnsf_nuhat, 1)
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p = model.p
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x1 = symbol("gnsf_x1", gnsf_nx1, 1)
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x1dot = symbol("gnsf_x1dot", gnsf_nx1, 1)
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z1 = symbol("gnsf_z1", gnsf_nz1, 1)
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dummy = symbol("gnsf_dummy", 1, 1)
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empty_var = symbol("gnsf_empty_var", 0, 0)
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## generate C code
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fun_name = model_name + '_gnsf_phi_fun'
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phi_fun_ = Function(fun_name, [y, uhat, p], [phi_fun(y, uhat, p)])
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phi_fun_.generate(fun_name, casadi_opts)
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fun_name = model_name + '_gnsf_phi_fun_jac_y'
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phi_fun_jac_y = model.phi_fun_jac_y
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phi_fun_jac_y_ = Function(fun_name, [y, uhat, p], phi_fun_jac_y(y, uhat, p))
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phi_fun_jac_y_.generate(fun_name, casadi_opts)
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fun_name = model_name + '_gnsf_phi_jac_y_uhat'
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phi_jac_y_uhat = model.phi_jac_y_uhat
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phi_jac_y_uhat_ = Function(fun_name, [y, uhat, p], phi_jac_y_uhat(y, uhat, p))
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phi_jac_y_uhat_.generate(fun_name, casadi_opts)
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fun_name = model_name + '_gnsf_f_lo_fun_jac_x1k1uz'
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f_lo_fun_jac_x1k1uz = model.f_lo_fun_jac_x1k1uz
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f_lo_fun_jac_x1k1uz_eval = f_lo_fun_jac_x1k1uz(x1, x1dot, z1, u, p)
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# avoid codegeneration issue
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if not isinstance(f_lo_fun_jac_x1k1uz_eval, tuple) and is_empty(f_lo_fun_jac_x1k1uz_eval):
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f_lo_fun_jac_x1k1uz_eval = [empty_var]
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f_lo_fun_jac_x1k1uz_ = Function(fun_name, [x1, x1dot, z1, u, p],
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f_lo_fun_jac_x1k1uz_eval)
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f_lo_fun_jac_x1k1uz_.generate(fun_name, casadi_opts)
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fun_name = model_name + '_gnsf_get_matrices_fun'
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get_matrices_fun_ = Function(fun_name, [dummy], get_matrices_fun(1))
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get_matrices_fun_.generate(fun_name, casadi_opts)
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# remove fields for json dump
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del model.phi_fun
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del model.phi_fun_jac_y
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del model.phi_jac_y_uhat
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del model.f_lo_fun_jac_x1k1uz
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del model.get_matrices_fun
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os.chdir(cwd)
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return
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