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435 lines
15 KiB
435 lines
15 KiB
# -*- coding: future_fstrings -*-
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#
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# Copyright (c) The acados authors.
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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, sys, json
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import urllib.request
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import shutil
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import numpy as np
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from casadi import SX, MX, DM, Function, CasadiMeta
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ALLOWED_CASADI_VERSIONS = ('3.5.6', '3.5.5', '3.5.4', '3.5.3', '3.5.2', '3.5.1', '3.4.5', '3.4.0')
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TERA_VERSION = "0.0.34"
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PLATFORM2TERA = {
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"linux": "linux",
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"darwin": "osx",
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"win32": "windows"
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}
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def get_acados_path():
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ACADOS_PATH = os.environ.get('ACADOS_SOURCE_DIR')
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if not ACADOS_PATH:
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acados_template_path = os.path.dirname(os.path.abspath(__file__))
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acados_path = os.path.join(acados_template_path, '..','..','..')
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ACADOS_PATH = os.path.realpath(acados_path)
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msg = 'Warning: Did not find environment variable ACADOS_SOURCE_DIR, '
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msg += 'guessed ACADOS_PATH to be {}.\n'.format(ACADOS_PATH)
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msg += 'Please export ACADOS_SOURCE_DIR to avoid this warning.'
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print(msg)
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return ACADOS_PATH
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def get_python_interface_path():
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ACADOS_PYTHON_INTERFACE_PATH = os.environ.get('ACADOS_PYTHON_INTERFACE_PATH')
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if not ACADOS_PYTHON_INTERFACE_PATH:
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acados_path = get_acados_path()
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ACADOS_PYTHON_INTERFACE_PATH = os.path.join(acados_path, 'interfaces', 'acados_template', 'acados_template')
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return ACADOS_PYTHON_INTERFACE_PATH
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def get_tera_exec_path():
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TERA_PATH = os.environ.get('TERA_PATH')
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if not TERA_PATH:
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TERA_PATH = os.path.join(get_acados_path(), 'bin', 't_renderer')
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if os.name == 'nt':
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TERA_PATH += '.exe'
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return TERA_PATH
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def check_casadi_version():
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casadi_version = CasadiMeta.version()
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if casadi_version in ALLOWED_CASADI_VERSIONS:
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return
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else:
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msg = 'Warning: Please note that the following versions of CasADi are '
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msg += 'officially supported: {}.\n '.format(" or ".join(ALLOWED_CASADI_VERSIONS))
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msg += 'If there is an incompatibility with the CasADi generated code, '
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msg += 'please consider changing your CasADi version.\n'
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msg += 'Version {} currently in use.'.format(casadi_version)
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print(msg)
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def is_column(x):
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if isinstance(x, np.ndarray):
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if x.ndim == 1:
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return True
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elif x.ndim == 2 and x.shape[1] == 1:
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return True
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else:
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return False
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elif isinstance(x, (MX, SX, DM)):
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if x.shape[1] == 1:
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return True
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elif x.shape[0] == 0 and x.shape[1] == 0:
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return True
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else:
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return False
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elif x == None or x == []:
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return False
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else:
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raise Exception("is_column expects one of the following types: np.ndarray, casadi.MX, casadi.SX."
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+ " Got: " + str(type(x)))
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def is_empty(x):
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if isinstance(x, (MX, SX, DM)):
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return x.is_empty()
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elif isinstance(x, np.ndarray):
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if np.prod(x.shape) == 0:
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return True
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else:
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return False
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elif x == None:
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return True
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elif isinstance(x, (set, list)):
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if len(x)==0:
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return True
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else:
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return False
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else:
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raise Exception("is_empty expects one of the following types: casadi.MX, casadi.SX, "
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+ "None, numpy array empty list, set. Got: " + str(type(x)))
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def casadi_length(x):
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if isinstance(x, (MX, SX, DM)):
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return int(np.prod(x.shape))
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else:
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raise Exception("casadi_length expects one of the following types: casadi.MX, casadi.SX."
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+ " Got: " + str(type(x)))
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def make_model_consistent(model):
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x = model.x
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xdot = model.xdot
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u = model.u
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z = model.z
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p = model.p
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if isinstance(x, MX):
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symbol = MX.sym
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elif isinstance(x, SX):
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symbol = SX.sym
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else:
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raise Exception("model.x must be casadi.SX or casadi.MX, got {}".format(type(x)))
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if is_empty(p):
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model.p = symbol('p', 0, 0)
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if is_empty(z):
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model.z = symbol('z', 0, 0)
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return model
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def get_lib_ext():
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lib_ext = '.so'
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if sys.platform == 'darwin':
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lib_ext = '.dylib'
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elif os.name == 'nt':
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lib_ext = ''
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return lib_ext
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def get_tera():
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tera_path = get_tera_exec_path()
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acados_path = get_acados_path()
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if os.path.exists(tera_path) and os.access(tera_path, os.X_OK):
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return tera_path
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repo_url = "https://github.com/acados/tera_renderer/releases"
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url = "{}/download/v{}/t_renderer-v{}-{}".format(
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repo_url, TERA_VERSION, TERA_VERSION, PLATFORM2TERA[sys.platform])
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manual_install = 'For manual installation follow these instructions:\n'
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manual_install += '1 Download binaries from {}\n'.format(url)
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manual_install += '2 Copy them in {}/bin\n'.format(acados_path)
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manual_install += '3 Strip the version and platform from the binaries: '
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manual_install += 'as t_renderer-v0.0.34-X -> t_renderer)\n'
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manual_install += '4 Enable execution privilege on the file "t_renderer" with:\n'
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manual_install += '"chmod +x {}"\n\n'.format(tera_path)
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msg = "\n"
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msg += 'Tera template render executable not found, '
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msg += 'while looking in path:\n{}\n'.format(tera_path)
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msg += 'In order to be able to render the templates, '
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msg += 'you need to download the tera renderer binaries from:\n'
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msg += '{}\n\n'.format(repo_url)
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msg += 'Do you wish to set up Tera renderer automatically?\n'
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msg += 'y/N? (press y to download tera or any key for manual installation)\n'
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if input(msg) == 'y':
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print("Dowloading {}".format(url))
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with urllib.request.urlopen(url) as response, open(tera_path, 'wb') as out_file:
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shutil.copyfileobj(response, out_file)
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print("Successfully downloaded t_renderer.")
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os.chmod(tera_path, 0o755)
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return tera_path
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msg_cancel = "\nYou cancelled automatic download.\n\n"
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msg_cancel += manual_install
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msg_cancel += "Once installed re-run your script.\n\n"
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print(msg_cancel)
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sys.exit(1)
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def render_template(in_file, out_file, output_dir, json_path, template_glob=None):
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acados_path = os.path.dirname(os.path.abspath(__file__))
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if template_glob is None:
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template_glob = os.path.join(acados_path, 'c_templates_tera', '**', '*')
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cwd = os.getcwd()
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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os.chdir(output_dir)
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tera_path = get_tera()
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# call tera as system cmd
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os_cmd = f"{tera_path} '{template_glob}' '{in_file}' '{json_path}' '{out_file}'"
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# Windows cmd.exe can not cope with '...', so use "..." instead:
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if os.name == 'nt':
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os_cmd = os_cmd.replace('\'', '\"')
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status = os.system(os_cmd)
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if (status != 0):
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raise Exception(f'Rendering of {in_file} failed!\n\nAttempted to execute OS command:\n{os_cmd}\n\n')
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os.chdir(cwd)
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## Conversion functions
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def make_object_json_dumpable(input):
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if isinstance(input, (np.ndarray)):
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return input.tolist()
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elif isinstance(input, (SX)):
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return input.serialize()
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elif isinstance(input, (MX)):
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# NOTE: MX expressions can not be serialized, only Functions.
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return input.__str__()
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elif isinstance(input, (DM)):
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return input.full()
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else:
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raise TypeError(f"Cannot make input of type {type(input)} dumpable.")
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def format_class_dict(d):
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"""
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removes the __ artifact from class to dict conversion
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"""
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out = {}
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for k, v in d.items():
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if isinstance(v, dict):
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v = format_class_dict(v)
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out_key = k.split('__', 1)[-1]
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out[k.replace(k, out_key)] = v
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return out
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def get_ocp_nlp_layout() -> dict:
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python_interface_path = get_python_interface_path()
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abs_path = os.path.join(python_interface_path, 'acados_layout.json')
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with open(abs_path, 'r') as f:
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ocp_nlp_layout = json.load(f)
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return ocp_nlp_layout
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def get_default_simulink_opts() -> dict:
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python_interface_path = get_python_interface_path()
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abs_path = os.path.join(python_interface_path, 'simulink_default_opts.json')
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with open(abs_path, 'r') as f:
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simulink_opts = json.load(f)
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return simulink_opts
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def J_to_idx(J):
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nrows = J.shape[0]
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idx = np.zeros((nrows, ))
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for i in range(nrows):
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this_idx = np.nonzero(J[i,:])[0]
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if len(this_idx) != 1:
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raise Exception('Invalid J matrix structure detected, ' \
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'must contain one nonzero element per row.')
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if this_idx.size > 0 and J[i,this_idx[0]] != 1:
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raise Exception('J matrices can only contain 1s.')
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idx[i] = this_idx[0]
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return idx
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def J_to_idx_slack(J):
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nrows = J.shape[0]
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ncol = J.shape[1]
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idx = np.zeros((ncol, ))
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i_idx = 0
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for i in range(nrows):
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this_idx = np.nonzero(J[i,:])[0]
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if len(this_idx) == 1:
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idx[i_idx] = i
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i_idx = i_idx + 1
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elif len(this_idx) > 1:
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raise Exception('J_to_idx_slack: Invalid J matrix. ' \
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'Found more than one nonzero in row ' + str(i))
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if this_idx.size > 0 and J[i,this_idx[0]] != 1:
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raise Exception('J_to_idx_slack: J matrices can only contain 1s, ' \
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'got J(' + str(i) + ', ' + str(this_idx[0]) + ') = ' + str(J[i,this_idx[0]]) )
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if not i_idx == ncol:
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raise Exception('J_to_idx_slack: J must contain a 1 in every column!')
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return idx
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def acados_dae_model_json_dump(model):
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# load model
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x = model.x
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xdot = model.xdot
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u = model.u
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z = model.z
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p = model.p
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f_impl = model.f_impl_expr
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model_name = model.name
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# create struct with impl_dae_fun, casadi_version
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fun_name = model_name + '_impl_dae_fun'
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impl_dae_fun = Function(fun_name, [x, xdot, u, z, p], [f_impl])
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casadi_version = CasadiMeta.version()
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str_impl_dae_fun = impl_dae_fun.serialize()
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dae_dict = {"str_impl_dae_fun": str_impl_dae_fun, "casadi_version": casadi_version}
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# dump
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json_file = model_name + '_acados_dae.json'
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with open(json_file, 'w') as f:
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json.dump(dae_dict, f, default=make_object_json_dumpable, indent=4, sort_keys=True)
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print("dumped ", model_name, " dae to file:", json_file, "\n")
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def set_up_imported_gnsf_model(acados_ocp):
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gnsf = acados_ocp.gnsf_model
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# check CasADi version
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# dump_casadi_version = gnsf['casadi_version']
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# casadi_version = CasadiMeta.version()
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# if not casadi_version == dump_casadi_version:
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# print("WARNING: GNSF model was dumped with another CasADi version.\n"
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# + "This might yield errors. Please use the same version for compatibility, serialize version: "
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# + dump_casadi_version + " current Python CasADi verison: " + casadi_version)
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# input("Press any key to attempt to continue...")
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# load model
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phi_fun = Function.deserialize(gnsf['phi_fun'])
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phi_fun_jac_y = Function.deserialize(gnsf['phi_fun_jac_y'])
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phi_jac_y_uhat = Function.deserialize(gnsf['phi_jac_y_uhat'])
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get_matrices_fun = Function.deserialize(gnsf['get_matrices_fun'])
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# obtain gnsf dimensions
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size_gnsf_A = get_matrices_fun.size_out(0)
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acados_ocp.dims.gnsf_nx1 = size_gnsf_A[1]
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acados_ocp.dims.gnsf_nz1 = size_gnsf_A[0] - size_gnsf_A[1]
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acados_ocp.dims.gnsf_nuhat = max(phi_fun.size_in(1))
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acados_ocp.dims.gnsf_ny = max(phi_fun.size_in(0))
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acados_ocp.dims.gnsf_nout = max(phi_fun.size_out(0))
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# save gnsf functions in model
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acados_ocp.model.phi_fun = phi_fun
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acados_ocp.model.phi_fun_jac_y = phi_fun_jac_y
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acados_ocp.model.phi_jac_y_uhat = phi_jac_y_uhat
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acados_ocp.model.get_matrices_fun = get_matrices_fun
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# get_matrices_fun = Function([model_name,'_gnsf_get_matrices_fun'], {dummy},...
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# {A, B, C, E, L_x, L_xdot, L_z, L_u, A_LO, c, E_LO, B_LO,...
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# nontrivial_f_LO, purely_linear, ipiv_x, ipiv_z, c_LO});
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get_matrices_out = get_matrices_fun(0)
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acados_ocp.model.gnsf['nontrivial_f_LO'] = int(get_matrices_out[12])
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acados_ocp.model.gnsf['purely_linear'] = int(get_matrices_out[13])
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if "f_lo_fun_jac_x1k1uz" in gnsf:
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f_lo_fun_jac_x1k1uz = Function.deserialize(gnsf['f_lo_fun_jac_x1k1uz'])
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acados_ocp.model.f_lo_fun_jac_x1k1uz = f_lo_fun_jac_x1k1uz
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else:
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dummy_var_x1 = SX.sym('dummy_var_x1', acados_ocp.dims.gnsf_nx1)
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dummy_var_x1dot = SX.sym('dummy_var_x1dot', acados_ocp.dims.gnsf_nx1)
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dummy_var_z1 = SX.sym('dummy_var_z1', acados_ocp.dims.gnsf_nz1)
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dummy_var_u = SX.sym('dummy_var_z1', acados_ocp.dims.nu)
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dummy_var_p = SX.sym('dummy_var_z1', acados_ocp.dims.np)
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empty_var = SX.sym('empty_var', 0, 0)
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empty_fun = Function('empty_fun', \
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[dummy_var_x1, dummy_var_x1dot, dummy_var_z1, dummy_var_u, dummy_var_p],
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[empty_var])
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acados_ocp.model.f_lo_fun_jac_x1k1uz = empty_fun
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del acados_ocp.gnsf_model
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def idx_perm_to_ipiv(idx_perm):
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n = len(idx_perm)
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vec = list(range(n))
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ipiv = np.zeros(n)
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print(n, idx_perm)
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# import pdb; pdb.set_trace()
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for ii in range(n):
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idx0 = idx_perm[ii]
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for jj in range(ii,n):
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if vec[jj]==idx0:
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idx1 = jj
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break
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tmp = vec[ii]
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vec[ii] = vec[idx1]
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vec[idx1] = tmp
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ipiv[ii] = idx1
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ipiv = ipiv-1 # C 0-based indexing
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return ipiv
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def print_casadi_expression(f):
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for ii in range(casadi_length(f)):
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print(f[ii,:])
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