3c38780d50
* wrap/python/spot.py: Introduce spot.translate (and spot.formula.translate) as well, as a wrapper around the spot.translator class. Also implement spot.tgba.show() to allow passing argument to dotty_reachable() before the result is converted to SVG. * wrap/python/tests/automata.ipynb: New test file. * wrap/python/tests/Makefile.am: Add it.
331 lines
12 KiB
Python
331 lines
12 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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from spot_impl import *
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import subprocess
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import sys
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_bdd_dict = make_bdd_dict()
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def _ostream_to_svg(ostr):
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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(ostr.str().encode('utf-8'))
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res = dotty.communicate()
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return res[0].decode('utf-8')
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def _render_automaton_as_svg(a, opt=""):
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ostr = ostringstream()
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dotty_reachable(ostr, a, opt)
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return _ostream_to_svg(ostr)
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tgba._repr_svg_ = _render_automaton_as_svg
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def _render_formula_as_svg(a):
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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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dotty(ostr, a)
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return SVG(_ostream_to_svg(ostr))
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def _render_tgba_as_svg(a, opt=""):
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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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return SVG(_render_automaton_as_svg(a, opt))
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tgba.show = _render_tgba_as_svg
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def _formula_str_ctor(self, str):
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self.this = parse_formula(str)
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def _formula_to_str(self, format = 'spot'):
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if format == 'spot':
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return to_string(self)
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elif format == 'spin':
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return to_spin_string(self)
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elif format == 'utf8':
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return to_utf8_string(self)
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elif format == 'lbt':
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return to_lbt_string(self)
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elif format == 'wring':
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return to_wring_string(self)
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elif format == 'latex':
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return to_latex_string(self)
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elif format == 'sclatex':
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return to_sclatex_string(self)
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else:
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raise ValueError("unknown string format: " + format)
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formula.__init__ = _formula_str_ctor
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formula.to_str = _formula_to_str
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formula.show_ast = _render_formula_as_svg
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def translate(formula, output='tgba', pref='small', level='high',
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complete=False):
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"""Translate a formula into an automaton.
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Keep in mind that pref expresses just a preference that may not be
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satisfied.
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Keyword arguments:
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output -- the type of automaton to build ('tgba', 'ba', 'monitor')
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pref -- prefered characteristic of the produced automaton
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('small', 'deterministic', 'any')
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level -- level of optimizations ('low', 'medium', 'high')
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complete -- whether to produce a complete automaton (True, False)
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"""
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if type(formula) == str:
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formula = parse_formula(formula)
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a = translator()
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if type(output) == str:
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output_ = output.lower()
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if output_ == 'tgba':
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output = postprocessor.TGBA
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elif output_ == 'ba':
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output = postprocessor.BA
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elif output_.startswith('mon'):
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output = postprocessor.Monitor
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else:
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raise ValueError("unknown output type: " + output)
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a.set_type(output)
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if complete:
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complete = postprocessor.Complete
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else:
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complete = 0
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if type(pref) == str:
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pref_ = pref.lower()
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if pref_.startswith('sm'):
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pref = postprocessor.Small
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elif pref_.startswith('det'):
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pref = postprocessor.Deterministic
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elif pref_ == 'any':
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pref = postprocessor.Any
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else:
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raise ValueError("unknown output preference: " + pref)
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a.set_pref(pref | complete)
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if type(level) == str:
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level_ = level.lower()
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if level_ == 'high':
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level = postprocessor.High
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elif level_.starswith('med'):
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level = postprocessor.Medium
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elif level_ == 'low':
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level = postprocessor.Low
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else:
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raise ValueError("unknown optimization level: " + level)
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a.set_level(level)
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return a.run(formula)
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formula.translate = translate
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# Wrapper around a formula iterator to which we add some methods of formula
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# (using _addfilter and _addmap), so that we can write things like
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# formulas.simplify().is_X_free().
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class formulaiterator:
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def __init__(self, formulas):
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self._formulas = formulas
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def __iter__(self):
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return self
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def __next__(self):
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return next(self._formulas)
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# fun shoud be a predicate and should be a method of formula.
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# _addfilter adds this predicate as a filter whith the same name in
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# formulaiterator.
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def _addfilter(fun):
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def filtf(self, *args, **kwargs):
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it = filter(lambda f: getattr(f, fun)(*args, **kwargs), self)
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return formulaiterator(it)
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def nfiltf(self, *args, **kwargs):
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it = filter(lambda f: not getattr(f, fun)(*args, **kwargs), self)
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return formulaiterator(it)
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setattr(formulaiterator, fun, filtf)
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if fun[:3] == 'is_':
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notfun = fun[:3] + 'not_' + fun[3:]
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elif fun[:4] == 'has_':
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notfun = fun[:4] + 'no_' + fun[4:]
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else:
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notfun = 'not_' + fun
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setattr(formulaiterator, fun, filtf)
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setattr(formulaiterator, notfun, nfiltf)
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# fun should be a function taking a formula as its first parameter and returning
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# a formula.
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# _addmap adds this function as a method of formula and formulaiterator.
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def _addmap(fun):
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def mapf(self, *args, **kwargs):
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return formulaiterator(map(lambda f: getattr(f, fun)(*args, **kwargs),
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self))
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setattr(formula, fun, lambda self, *args, **kwargs: globals()[fun](self,
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*args, **kwargs))
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setattr(formulaiterator, fun, mapf)
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def randltl(ap, n = -1, **kwargs):
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"""Generate random formulas.
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Returns a random formula iterator.
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ap: the number of atomic propositions used to generate random formulas.
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n: number of formulas to generate, or unbounded if n < 0.
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**kwargs:
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seed: seed for the random number generator (0).
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output: can be 'ltl', 'psl', 'bool' or 'sere' ('ltl').
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allow_dups: allow duplicate formulas (False).
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tree_size: tree size of the formulas generated, before mandatory
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simplifications (15)
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boolean_priorities: set priorities for Boolean formulas.
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ltl_priorities: set priorities for LTL formulas.
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sere_priorities: set priorities for SERE formulas.
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dump_priorities: show current priorities, do not generate any formula.
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simplify:
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0 No rewriting
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1 basic rewritings and eventual/universal rules
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2 additional syntactic implication rules
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3 (default) better implications using containment
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"""
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opts = option_map()
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output_map = {
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"ltl" : OUTPUTLTL,
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"psl" : OUTPUTPSL,
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"bool" : OUTPUTBOOL,
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"sere" : OUTPUTSERE
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}
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if isinstance(ap, list):
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aprops = atomic_prop_set()
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e = default_environment.instance()
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for elt in ap:
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aprops.insert(is_atomic_prop(e.require(elt)))
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ap = aprops
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ltl_priorities = kwargs.get("ltl_priorities", None)
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sere_priorities = kwargs.get("sere_priorities", None)
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boolean_priorities = kwargs.get("boolean_priorities", None)
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output = output_map[kwargs.get("output", "ltl")]
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opts.set("output", output)
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opts.set("seed", kwargs.get("seed", 0))
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tree_size = kwargs.get("tree_size", 15)
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if isinstance(tree_size, tuple):
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tree_size_min, tree_size_max = tree_size
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else:
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tree_size_min = tree_size_max = tree_size
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opts.set("tree_size_min", tree_size_min)
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opts.set("tree_size_max", tree_size_max)
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opts.set("unique", not kwargs.get("allow_dups", False))
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opts.set("wf", kwargs.get("weak_fairness", False))
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simpl_level = kwargs.get("simplify", 0)
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if simpl_level > 3 or simpl_level < 0:
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sys.stderr.write('invalid simplification level: ' + simpl_level)
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return
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opts.set("simplification_level", simpl_level)
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rg = randltlgenerator(ap, opts, ltl_priorities, sere_priorities,
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boolean_priorities)
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dump_priorities = kwargs.get("dump_priorities", False)
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if dump_priorities:
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dumpstream = ostringstream()
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if output == OUTPUTLTL:
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print('Use argument ltl_priorities=STRING to set the following ' \
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'LTL priorities:\n')
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rg.dump_ltl_priorities(dumpstream)
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print(dumpstream.str())
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elif output == OUTPUTBOOL:
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print('Use argument boolean_priorities=STRING to set the ' \
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'following Boolean formula priorities:\n')
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rg.dump_bool_priorities(dumpstream)
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print(dumpstream.str())
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elif output == OUTPUTPSL or output == OUTPUTSERE:
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if output != OUTPUTSERE:
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print('Use argument ltl_priorities=STRING to set the following ' \
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'LTL priorities:\n')
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rg.dump_psl_priorities(dumpstream)
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print(dumpstream.str())
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print('Use argument sere_priorities=STRING to set the following ' \
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'SERE priorities:\n')
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rg.dump_sere_priorities(dumpstream)
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print(dumpstream.str())
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print('Use argument boolean_priorities=STRING to set the ' \
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'following Boolean formula priorities:\n')
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rg.dump_sere_bool_priorities(dumpstream)
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print(dumpstream.str())
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else:
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sys.stderr.write("internal error: unknown type of output")
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return
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def _randltlgenerator(rg):
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i = 0
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while i != n:
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f = rg.next()
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if f is None:
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sys.stderr.write("Warning: could not generate a new unique formula " \
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"after " + str(MAX_TRIALS) + " trials.\n")
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yield None
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else:
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yield f
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i += 1
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return formulaiterator(_randltlgenerator(rg))
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def simplify(f, **kwargs):
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level = kwargs.get('level', None)
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if level is not None:
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return ltl_simplifier(ltl_simplifier_options(level)).simplify(f)
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basics = kwargs.get('basics', True)
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synt_impl = kwargs.get('synt_impl', True)
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event_univ = kwargs.get('event_univ', True)
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containment_checks = kwargs.get('containment_checks', False)
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containment_checks_stronger = kwargs.get('containment_checks_stronger', False)
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nenoform_stop_on_boolean = kwargs.get('nenoform_stop_on_boolean', False)
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reduce_size_strictly = kwargs.get('reduce_size_strictly', False)
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boolean_to_isop = kwargs.get('boolean_to_isop', False)
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favor_event_univ = kwargs.get('favor_event_univ', False)
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simp_opts = ltl_simplifier_options(basics,
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synt_impl,
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event_univ,
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containment_checks,
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containment_checks_stronger,
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nenoform_stop_on_boolean,
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reduce_size_strictly,
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boolean_to_isop,
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favor_event_univ)
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return ltl_simplifier(simp_opts).simplify(f)
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for fun in dir(formula):
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if (callable(getattr(formula, fun)) and
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(fun[:3] == 'is_' or fun[:4] == 'has_')):
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_addfilter(fun)
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for fun in ['remove_x', 'get_literal', 'relabel', 'relabel_bse',
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'simplify', 'unabbreviate_ltl']:
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_addmap(fun)
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