808 lines
28 KiB
Python
808 lines
28 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2014, 2015 Laboratoire de
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# Recherche et Développement de l'Epita (LRDE).
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#
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# This file is part of Spot, a model checking library.
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#
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# Spot is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by
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# the Free Software Foundation; either version 3 of the License, or
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# (at your option) any later version.
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#
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# Spot is distributed in the hope that it will be useful, but WITHOUT
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# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
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# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
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# License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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import sys
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if sys.hexversion < 0x03030000:
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sys.exit("This module requires Python 3.3 or newer")
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from spot_impl import *
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import subprocess
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import os
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import signal
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from functools import lru_cache
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def _extend(*classes):
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"""
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Decorator that extends all the given classes with the contents
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of the class currently being defined.
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"""
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def wrap(this):
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for cls in classes:
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for (name, val) in this.__dict__.items():
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if name not in ('__dict__', '__weakref__') \
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and not (name == '__doc__' and val is None):
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setattr(cls, name, val)
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return classes[0]
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return wrap
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def setup(**kwargs):
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"""Configure Spot for fancy display.
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This is manly useful in Jupyter/IPython.
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Note that this function needs to be called before any automaton is
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displayed. Afterwards it will have no effect (you should restart
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Python, or the Jupyter/IPython Kernel).
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Parameters
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----------
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bullets : bool
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whether to display acceptance conditions as UTF8 bullets
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(default: True)
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fillcolor : str
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the color to use for states (default: '#ffffaa')
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size : str
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the width and height of the GraphViz output in inches
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(default: '10.2,5')
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font : str
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the font to use in the GraphViz output (default: 'Lato')
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"""
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import os
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s = ('size="{}" node[style=filled,fillcolor="{}"] '
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'edge[arrowhead=vee, arrowsize=.7]')
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os.environ['SPOT_DOTEXTRA'] = s.format(kwargs.get('size', '10.2,5'),
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kwargs.get('fillcolor', '#ffffaa'))
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bullets = 'B' if kwargs.get('bullets', True) else ''
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d = 'rf({})'.format(kwargs.get('font', 'Lato')) + bullets
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os.environ['SPOT_DOTDEFAULT'] = d
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# In version 3.0.2, Swig puts strongly typed enum in the main
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# namespace without prefixing them. Latter versions fix this. So we
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# can remove for following hack once 3.0.2 is no longer used in our
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# build farm.
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if 'op_ff' not in globals():
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for i in ('ff', 'tt', 'eword', 'ap', 'Not', 'X', 'F', 'G',
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'Closure', 'NegClosure', 'NegClosureMarked',
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'Xor', 'Implies', 'Equiv', 'U', 'R', 'W', 'M',
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'EConcat', 'EConcatMarked', 'UConcat', 'Or',
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'OrRat', 'And', 'AndRat', 'AndNLM', 'Concat',
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'Fusion', 'Star', 'FStar'):
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globals()['op_' + i] = globals()[i]
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del globals()[i]
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# Global BDD dict so that we do not have to create one in user code.
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_bdd_dict = make_bdd_dict()
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# Add a small LRU cache so that when we display automata into a
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# interactive widget, we avoid some repeated calls to dot for
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# identical inputs.
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@lru_cache(maxsize=64)
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def _str_to_svg(str):
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dotty = subprocess.Popen(['dot', '-Tsvg'],
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stdin=subprocess.PIPE,
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stdout=subprocess.PIPE)
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dotty.stdin.write(str)
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res = dotty.communicate()
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return res[0].decode('utf-8')
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def _ostream_to_svg(ostr):
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return _str_to_svg(ostr.str().encode('utf-8'))
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@_extend(twa, ta)
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class twa:
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def _repr_svg_(self, opt=None):
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"""Output the automaton as SVG"""
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ostr = ostringstream()
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print_dot(ostr, self, opt)
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return _ostream_to_svg(ostr)
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def show(self, opt=None):
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"""Display the automaton as SVG, in the IPython/Jupyter notebook"""
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# Load the SVG function only if we need it. This way the
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# bindings can still be used outside of IPython if IPython is
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# not installed.
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from IPython.display import SVG
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return SVG(self._repr_svg_(opt))
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@_extend(twa)
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class twa:
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def to_str(a, format='hoa', opt=None):
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format = format.lower()
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if format == 'hoa':
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ostr = ostringstream()
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print_hoa(ostr, a, opt)
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return ostr.str()
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if format == 'dot':
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ostr = ostringstream()
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print_dot(ostr, a, opt)
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return ostr.str()
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if format == 'spin':
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ostr = ostringstream()
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print_never_claim(ostr, a, opt)
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return ostr.str()
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if format == 'lbtt':
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ostr = ostringstream()
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print_lbtt(ostr, a, opt)
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return ostr.str()
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raise ValueError("unknown string format: " + format)
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def save(a, filename, format='hoa', opt=None, append=False):
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with open(filename, 'a' if append else 'w') as f:
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s = a.to_str(format, opt)
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f.write(s)
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if s[-1] != '\n':
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f.write('\n')
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return a
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@_extend(formula)
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class formula:
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def __init__(self, str):
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"""Parse the given string to create a formula."""
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self.this = parse_formula(str)
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def show_ast(self):
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"""Display the syntax tree of the formula."""
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# Load the SVG function only if we need it. This way the bindings
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# can still be used outside of IPython if IPython is not
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# installed.
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from IPython.display import SVG
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ostr = ostringstream()
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print_dot_psl(ostr, self)
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return SVG(_ostream_to_svg(ostr))
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def to_str(self, format='spot', parenth=False):
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if format == 'spot' or format == 'f':
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return str_psl(self, parenth)
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elif format == 'spin' or format == 's':
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return str_spin_ltl(self, parenth)
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elif format == 'utf8' or format == '8':
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return str_utf8_psl(self, parenth)
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elif format == 'lbt' or format == 'l':
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return str_lbt_ltl(self)
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elif format == 'wring' or format == 'w':
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return str_wring_ltl(self)
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elif format == 'latex' or format == 'x':
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return str_latex_psl(self, parenth)
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elif format == 'sclatex' or format == 'X':
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return str_sclatex_psl(self, parenth)
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else:
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raise ValueError("unknown string format: " + format)
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def __format__(self, spec):
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"""Format the formula according to `spec`.
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Parameters
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----------
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spec : str, optional
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a list of letters that specify how the formula
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should be formatted.
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Supported specifiers
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--------------------
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- 'f': use Spot's syntax (default)
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- '8': use Spot's syntax in UTF-8 mode
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- 's': use Spin's syntax
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- 'l': use LBT's syntax
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- 'w': use Wring's syntax
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- 'x': use LaTeX output
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- 'X': use self-contained LaTeX output
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Add some of those letters for additional options:
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- 'p': use full parentheses
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- 'c': escape the formula for CSV output (this will
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enclose the formula in double quotes, and escape
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any included double quotes)
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- 'h': escape the formula for HTML output
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- 'd': escape double quotes and backslash,
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for use in C-strings (the outermost double
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quotes are *not* added)
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- 'q': quote and escape for shell output, using single
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quotes or double quotes depending on the contents.
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- ':spec': pass the remaining specification to the
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formating function for strings.
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"""
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syntax = 'f'
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parent = False
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escape = None
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while spec:
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c, spec = spec[0], spec[1:]
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if c in ('f', 's', '8', 'l', 'w', 'x', 'X'):
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syntax = c
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elif c == 'p':
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parent = True
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elif c in ('c', 'd', 'h', 'q'):
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escape = c
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elif c == ':':
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break
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else:
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raise ValueError("unknown format specification: " + c + spec)
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s = self.to_str(syntax, parent)
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if escape == 'c':
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o = ostringstream()
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escape_rfc4180(o, s)
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s = '"' + o.str() + '"'
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elif escape == 'd':
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s = escape_str(s)
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elif escape == 'h':
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o = ostringstream()
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escape_html(o, s)
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s = o.str()
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elif escape == 'q':
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o = ostringstream()
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quote_shell_string(o, s)
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s = o.str()
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return s.__format__(spec)
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def traverse(self, func):
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if func(self):
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return
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for f in self:
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f.traverse(func)
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def map(self, func):
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k = self.kind()
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if k in (op_ff, op_tt, op_eword, op_ap):
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return self
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if k in (op_Not, op_X, op_F, op_G, op_Closure,
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op_NegClosure, op_NegClosureMarked):
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return formula.unop(k, func(self[0]))
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if k in (op_Xor, op_Implies, op_Equiv, op_U, op_R, op_W,
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op_M, op_EConcat, op_EConcatMarked, op_UConcat):
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return formula.binop(k, func(self[0]), func(self[1]))
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if k in (op_Or, op_OrRat, op_And, op_AndRat, op_AndNLM,
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op_Concat, op_Fusion):
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return formula.multop(k, [func(x) for x in self])
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if k in (op_Star, op_FStar):
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return formula.bunop(k, func(self[0]), self.min(), self.max())
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raise ValueError("unknown type of formula")
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def automata(*sources, timeout=None):
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"""Read automata from a list of sources.
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Parameters
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----------
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*sources : list of str
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These sources can be either commands (end with `|`),
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textual represantations of automata (contain `\n`),
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or filenames (else).
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timeout_error : int, optional
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Number of seconds to wait for the result of a command.
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If None (the default), not limit is used.
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Notes
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-----
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The automata can be written in the `HOA format`_, as `never
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claims`_, in `LBTT's format`_, or in `ltl2dstar's format`_.
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.. _HOA format: http://adl.github.io/hoaf/
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.. _never claims: http://spinroot.com/spin/Man/never.html
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.. _LBTT's format:
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http://www.tcs.hut.fi/Software/lbtt/doc/html/Format-for-automata.html
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.. _ltl2dstar's format:
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http://www.ltl2dstar.de/docs/ltl2dstar.html#output-format-dstar
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If an argument ends with a `|`, then this argument is interpreted as
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a shell command, and the output of that command (without the `|`)
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is parsed.
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If an argument contains a newline, then it is interpreted as
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actual contents to be parsed.
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Otherwise, the argument is assumed to be a filename.
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The result of this function is a generator on all the automata
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objects read from these sources. The typical usage is::
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for aut in spot.automata(filename, command, ...):
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# do something with aut
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When the source is a command, and no `timeout` is specified,
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parsing is done straight out of the pipe connecting the
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command. So
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for aut in spot.automata('randaut -H -n 10 2 |'):
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process(aut)
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will call `process(aut)` on each automaton as soon as it is output by
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`randaut`, and without waiting for `randaut` to terminate.
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However if `timeout` is passed, then `automata()` will wait for
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the entire command to terminate before parsing its entire output.
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If one command takes more than `timeout` seconds,
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`subprocess.TimeoutExpired` is raised.
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If any command terminates with a non-zero error,
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`subprocess.CalledProcessError` is raised.
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"""
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for filename in sources:
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try:
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p = None
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proc = None
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if filename[-1] == '|':
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# universal_newlines for str output instead of bytes
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# when the pipe is read from Python (which happens
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# when timeout is set).
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proc = subprocess.Popen(filename[:-1], shell=True,
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preexec_fn=os.setsid,
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universal_newlines=True,
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stdout=subprocess.PIPE)
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if timeout is None:
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p = automaton_stream_parser(proc.stdout.fileno(),
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filename, True)
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else:
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try:
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out, err = proc.communicate(timeout=timeout)
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except subprocess.TimeoutExpired:
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# Using subprocess.check_output() with timeout
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# would just kill the shell, not its children.
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os.killpg(proc.pid, signal.SIGKILL)
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raise
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else:
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ret = proc.wait()
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if ret:
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raise subprocess.CalledProcessError(ret,
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filename[:-1])
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finally:
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proc = None
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p = automaton_stream_parser(out, filename, True)
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elif '\n' in filename:
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p = automaton_stream_parser(filename, "<string>", True)
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else:
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p = automaton_stream_parser(filename, True)
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a = True
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while a:
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# This returns None when we reach the end of the file.
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a = p.parse_strict(_bdd_dict)
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if a:
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yield a
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finally:
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# Make sure we destroy the parser (p) and the subprocess
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# (prop) in the correct order...
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del p
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if proc is not None:
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if not a:
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# We reached the end of the stream. Wait for the
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# process to finish, so that we get its exit code.
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ret = proc.wait()
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else:
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# if a != None, we probably got there through an
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# exception, and the subprocess might still be
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# running. Check if an exit status is available
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# just in case.
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ret = proc.poll()
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del proc
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if ret:
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raise subprocess.CalledProcessError(ret, filename[:-1])
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return
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def automaton(filename, **kwargs):
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"""Read a single automaton from a file.
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See `spot.automata` for a list of supported formats."""
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try:
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return next(automata(filename, **kwargs))
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except StopIteration:
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raise RuntimeError("Failed to read automaton from {}".format(filename))
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def _postproc_translate_options(obj, default_type, *args):
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type_ = None
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pref_ = None
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optm_ = None
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comp_ = 0
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unam_ = 0
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sbac_ = 0
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def type_set(val):
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nonlocal type_
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if type_ is not None and type_ != val:
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raise ValueError("type cannot be both {} and {}"
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.format(type_, val))
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elif val == 'generic':
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type_ = postprocessor.Generic
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elif val == 'tgba':
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type_ = postprocessor.TGBA
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elif val == 'ba':
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type_ = postprocessor.BA
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else:
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assert(val == 'monitor')
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type_ = postprocessor.Monitor
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def pref_set(val):
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nonlocal pref_
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if pref_ is not None and pref_ != val:
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raise ValueError("preference cannot be both {} and {}"
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.format(pref_, val))
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elif val == 'small':
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pref_ = postprocessor.Small
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elif val == 'deterministic':
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pref_ = postprocessor.Deterministic
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else:
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assert(val == 'any')
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pref_ = postprocessor.Any
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def optm_set(val):
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nonlocal optm_
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if optm_ is not None and optm_ != val:
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raise ValueError("optimization level cannot be both {} and {}"
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.format(optm_, val))
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if val == 'high':
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optm_ = postprocessor.High
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elif val.startswith('med'):
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optm_ = postprocessor.Medium
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else:
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assert(val == 'low')
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optm_ = postprocessor.Low
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def misc_set(val):
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nonlocal comp_, unam_, sbac_
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if val == 'complete':
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comp_ = postprocessor.Complete
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elif val == 'sbacc' or val == 'state-based-acceptance':
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sbac_ = postprocessor.SBAcc
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else:
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assert(val == 'unambiguous')
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unam_ = postprocessor.Unambiguous
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options = {
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'tgba': type_set,
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'ba': type_set,
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'monitor': type_set,
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'generic': type_set,
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'small': pref_set,
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'deterministic': pref_set,
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'any': pref_set,
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'high': optm_set,
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'medium': optm_set,
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'low': optm_set,
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'complete': misc_set,
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'unambiguous': misc_set,
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'statebasedacceptance': misc_set,
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'sbacc': misc_set,
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}
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for arg in args:
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arg = arg.lower()
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fn = options.get(arg)
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if fn:
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fn(arg)
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else:
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# arg is not an know option, but maybe it is a prefix of
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# one of them
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compat = []
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f = None
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for key, fn in options.items():
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if key.startswith(arg):
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compat.append(key)
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f = fn
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lc = len(compat)
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if lc == 1:
|
|
f(compat[0])
|
|
elif lc < 1:
|
|
raise ValueError("unknown option '{}'".format(arg))
|
|
else:
|
|
raise ValueError("ambiguous option '{}' is prefix of {}"
|
|
.format(arg, str(compat)))
|
|
|
|
if type_ is None:
|
|
type_ = default_type
|
|
if pref_ is None:
|
|
pref_ = postprocessor.Small
|
|
if optm_ is None:
|
|
optm_ = postprocessor.High
|
|
|
|
obj.set_type(type_)
|
|
obj.set_pref(pref_ | comp_ | unam_ | sbac_)
|
|
obj.set_level(optm_)
|
|
|
|
|
|
def translate(formula, *args):
|
|
"""Translate a formula into an automaton.
|
|
|
|
Keep in mind that 'Deterministic' expresses just a preference that
|
|
may not be satisfied.
|
|
|
|
The optional arguments should be strings among the following:
|
|
- at most one in 'TGBA', 'BA', or 'Monitor'
|
|
(type of automaton to build)
|
|
- at most one in 'Small', 'Deterministic', 'Any'
|
|
(preferred characteristics of the produced automaton)
|
|
- at most one in 'Low', 'Medium', 'High'
|
|
(optimization level)
|
|
- any combination of 'Complete', 'Unambiguous', and
|
|
'StateBasedAcceptance' (or 'SBAcc' for short)
|
|
|
|
The default corresponds to 'tgba', 'small' and 'high'.
|
|
"""
|
|
a = translator(_bdd_dict)
|
|
_postproc_translate_options(a, postprocessor.TGBA, *args)
|
|
if type(formula) == str:
|
|
formula = parse_formula(formula)
|
|
return a.run(formula)
|
|
|
|
|
|
formula.translate = translate
|
|
|
|
|
|
def postprocess(automaton, *args):
|
|
"""Post process an automaton.
|
|
|
|
This applies a number of simlification algorithms, depending on
|
|
the options supplied. Keep in mind that 'Deterministic' expresses
|
|
just a preference that may not be satisfied if the input is
|
|
not already 'Deterministic'.
|
|
|
|
The optional arguments should be strings among the following:
|
|
- at most one in 'Generic', 'TGBA', 'BA', or 'Monitor'
|
|
(type of automaton to build)
|
|
- at most one in 'Small', 'Deterministic', 'Any'
|
|
(preferred characteristics of the produced automaton)
|
|
- at most one in 'Low', 'Medium', 'High'
|
|
(optimization level)
|
|
- any combination of 'Complete' and 'StateBasedAcceptance'
|
|
(or 'SBAcc' for short)
|
|
|
|
The default corresponds to 'generic', 'small' and 'high'.
|
|
"""
|
|
p = postprocessor()
|
|
if type(automaton) == str:
|
|
automaton = globals()['automaton'](automaton)
|
|
_postproc_translate_options(p, postprocessor.Generic, *args)
|
|
return p.run(automaton)
|
|
|
|
|
|
twa.postprocess = postprocess
|
|
|
|
# Wrap C++-functions into lambdas so that they get converted into
|
|
# instance methods (i.e., self passed as first argument
|
|
# automatically), because only used-defined functions are converted as
|
|
# instance methods.
|
|
for meth in ('scc_filter', 'scc_filter_states'):
|
|
setattr(twa_graph, meth, (lambda self, *args, **kwargs:
|
|
globals()[meth](self, *args, **kwargs)))
|
|
|
|
# Wrapper around a formula iterator to which we add some methods of formula
|
|
# (using _addfilter and _addmap), so that we can write things like
|
|
# formulas.simplify().is_X_free().
|
|
class formulaiterator:
|
|
def __init__(self, formulas):
|
|
self._formulas = formulas
|
|
|
|
def __iter__(self):
|
|
return self
|
|
|
|
def __next__(self):
|
|
return next(self._formulas)
|
|
|
|
|
|
# fun shoud be a predicate and should be a method of formula.
|
|
# _addfilter adds this predicate as a filter whith the same name in
|
|
# formulaiterator.
|
|
def _addfilter(fun):
|
|
def filtf(self, *args, **kwargs):
|
|
it = filter(lambda f: getattr(f, fun)(*args, **kwargs), self)
|
|
return formulaiterator(it)
|
|
|
|
def nfiltf(self, *args, **kwargs):
|
|
it = filter(lambda f: not getattr(f, fun)(*args, **kwargs), self)
|
|
return formulaiterator(it)
|
|
|
|
if fun[:3] == 'is_':
|
|
notfun = 'is_not_' + fun[3:]
|
|
elif fun[:4] == 'has_':
|
|
notfun = 'has_no_' + fun[4:]
|
|
else:
|
|
notfun = 'not_' + fun
|
|
setattr(formulaiterator, fun, filtf)
|
|
setattr(formulaiterator, notfun, nfiltf)
|
|
|
|
|
|
# fun should be a function taking a formula as its first parameter and
|
|
# returning a formula. _addmap adds this function as a method of
|
|
# formula and formulaiterator.
|
|
def _addmap(fun):
|
|
def mapf(self, *args, **kwargs):
|
|
return formulaiterator(map(lambda f: getattr(f, fun)(*args, **kwargs),
|
|
self))
|
|
setattr(formula, fun,
|
|
lambda self, *args, **kwargs:
|
|
globals()[fun](self, *args, **kwargs))
|
|
setattr(formulaiterator, fun, mapf)
|
|
|
|
|
|
def randltl(ap, n=-1, **kwargs):
|
|
"""Generate random formulas.
|
|
|
|
Returns a random formula iterator.
|
|
|
|
ap: the number of atomic propositions used to generate random formulas.
|
|
|
|
n: number of formulas to generate, or unbounded if n < 0.
|
|
|
|
**kwargs:
|
|
seed: seed for the random number generator (0).
|
|
output: can be 'ltl', 'psl', 'bool' or 'sere' ('ltl').
|
|
allow_dups: allow duplicate formulas (False).
|
|
tree_size: tree size of the formulas generated, before mandatory
|
|
simplifications (15)
|
|
boolean_priorities: set priorities for Boolean formulas.
|
|
ltl_priorities: set priorities for LTL formulas.
|
|
sere_priorities: set priorities for SERE formulas.
|
|
dump_priorities: show current priorities, do not generate any formula.
|
|
simplify:
|
|
0 No rewriting
|
|
1 basic rewritings and eventual/universal rules
|
|
2 additional syntactic implication rules
|
|
3 (default) better implications using containment
|
|
"""
|
|
opts = option_map()
|
|
output_map = {
|
|
"ltl": OUTPUTLTL,
|
|
"psl": OUTPUTPSL,
|
|
"bool": OUTPUTBOOL,
|
|
"sere": OUTPUTSERE
|
|
}
|
|
|
|
if isinstance(ap, list):
|
|
aprops = atomic_prop_set()
|
|
for elt in ap:
|
|
aprops.insert(formula.ap(elt))
|
|
ap = aprops
|
|
ltl_priorities = kwargs.get("ltl_priorities", None)
|
|
sere_priorities = kwargs.get("sere_priorities", None)
|
|
boolean_priorities = kwargs.get("boolean_priorities", None)
|
|
output = output_map[kwargs.get("output", "ltl")]
|
|
opts.set("output", output)
|
|
opts.set("seed", kwargs.get("seed", 0))
|
|
tree_size = kwargs.get("tree_size", 15)
|
|
if isinstance(tree_size, tuple):
|
|
tree_size_min, tree_size_max = tree_size
|
|
else:
|
|
tree_size_min = tree_size_max = tree_size
|
|
opts.set("tree_size_min", tree_size_min)
|
|
opts.set("tree_size_max", tree_size_max)
|
|
opts.set("unique", not kwargs.get("allow_dups", False))
|
|
opts.set("wf", kwargs.get("weak_fairness", False))
|
|
simpl_level = kwargs.get("simplify", 0)
|
|
if simpl_level > 3 or simpl_level < 0:
|
|
sys.stderr.write('invalid simplification level: ' + simpl_level)
|
|
return
|
|
opts.set("simplification_level", simpl_level)
|
|
|
|
rg = randltlgenerator(ap, opts, ltl_priorities, sere_priorities,
|
|
boolean_priorities)
|
|
|
|
dump_priorities = kwargs.get("dump_priorities", False)
|
|
if dump_priorities:
|
|
dumpstream = ostringstream()
|
|
if output == OUTPUTLTL:
|
|
print('Use argument ltl_priorities=STRING to set the following '
|
|
'LTL priorities:\n')
|
|
rg.dump_ltl_priorities(dumpstream)
|
|
print(dumpstream.str())
|
|
elif output == OUTPUTBOOL:
|
|
print('Use argument boolean_priorities=STRING to set the '
|
|
'following Boolean formula priorities:\n')
|
|
rg.dump_bool_priorities(dumpstream)
|
|
print(dumpstream.str())
|
|
elif output == OUTPUTPSL or output == OUTPUTSERE:
|
|
if output != OUTPUTSERE:
|
|
print('Use argument ltl_priorities=STRING to set the '
|
|
'following LTL priorities:\n')
|
|
rg.dump_psl_priorities(dumpstream)
|
|
print(dumpstream.str())
|
|
print('Use argument sere_priorities=STRING to set the '
|
|
'following SERE priorities:\n')
|
|
rg.dump_sere_priorities(dumpstream)
|
|
print(dumpstream.str())
|
|
print('Use argument boolean_priorities=STRING to set the '
|
|
'following Boolean formula priorities:\n')
|
|
rg.dump_sere_bool_priorities(dumpstream)
|
|
print(dumpstream.str())
|
|
else:
|
|
sys.stderr.write("internal error: unknown type of output")
|
|
return
|
|
|
|
class _randltliterator:
|
|
def __init__(self, rg, n):
|
|
self.rg = rg
|
|
self.i = 0
|
|
self.n = n
|
|
|
|
def __iter__(self):
|
|
return self
|
|
|
|
def __next__(self):
|
|
if self.i == self.n:
|
|
raise StopIteration
|
|
f = self.rg.next()
|
|
if f is None:
|
|
sys.stderr.write("Warning: could not generate a new "
|
|
"unique formula after {} trials.\n"
|
|
.format(MAX_TRIALS))
|
|
raise StopIteration
|
|
self.i += 1
|
|
return f
|
|
|
|
return formulaiterator(_randltliterator(rg, n))
|
|
|
|
|
|
def simplify(f, **kwargs):
|
|
level = kwargs.get('level', None)
|
|
if level is not None:
|
|
return tl_simplifier(tl_simplifier_options(level)).simplify(f)
|
|
|
|
basics = kwargs.get('basics', True)
|
|
synt_impl = kwargs.get('synt_impl', True)
|
|
event_univ = kwargs.get('event_univ', True)
|
|
cont_checks = kwargs.get('containment_checks', False)
|
|
cont_checks_stronger = kwargs.get('containment_checks_stronger', False)
|
|
nenoform_stop_on_boolean = kwargs.get('nenoform_stop_on_boolean', False)
|
|
reduce_size_strictly = kwargs.get('reduce_size_strictly', False)
|
|
boolean_to_isop = kwargs.get('boolean_to_isop', False)
|
|
favor_event_univ = kwargs.get('favor_event_univ', False)
|
|
|
|
simp_opts = tl_simplifier_options(basics,
|
|
synt_impl,
|
|
event_univ,
|
|
cont_checks,
|
|
cont_checks_stronger,
|
|
nenoform_stop_on_boolean,
|
|
reduce_size_strictly,
|
|
boolean_to_isop,
|
|
favor_event_univ)
|
|
return tl_simplifier(simp_opts).simplify(f)
|
|
|
|
|
|
for fun in dir(formula):
|
|
if (callable(getattr(formula, fun)) and (fun[:3] == 'is_' or
|
|
fun[:4] == 'has_')):
|
|
_addfilter(fun)
|
|
|
|
for fun in ['remove_x', 'relabel', 'relabel_bse',
|
|
'simplify', 'unabbreviate']:
|
|
_addmap(fun)
|