# -*- coding: utf-8 -*- # Copyright (C) 2014, 2015 Laboratoire de # Recherche et Développement de l'Epita (LRDE). # # This file is part of Spot, a model checking library. # # Spot is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 3 of the License, or # (at your option) any later version. # # Spot is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY # or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public # License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . from spot_impl import * import subprocess import sys _bdd_dict = make_bdd_dict() def _ostream_to_svg(ostr): dotty = subprocess.Popen(['dot', '-Tsvg'], stdin=subprocess.PIPE, stdout=subprocess.PIPE) dotty.stdin.write(ostr.str().encode('utf-8')) res = dotty.communicate() return res[0].decode('utf-8') def _render_automaton_as_svg(a, opt=""): ostr = ostringstream() dotty_reachable(ostr, a, opt) return _ostream_to_svg(ostr) tgba._repr_svg_ = _render_automaton_as_svg def _render_formula_as_svg(a): # Load the SVG function only if we need it. This way the bindings # can still be used outside of IPython if IPython is not # installed. from IPython.display import SVG ostr = ostringstream() dotty(ostr, a) return SVG(_ostream_to_svg(ostr)) def _render_tgba_as_svg(a, opt=""): # Load the SVG function only if we need it. This way the bindings # can still be used outside of IPython if IPython is not # installed. from IPython.display import SVG return SVG(_render_automaton_as_svg(a, opt)) tgba.show = _render_tgba_as_svg def _formula_str_ctor(self, str): self.this = parse_formula(str) def _formula_to_str(self, format = 'spot'): if format == 'spot': return to_string(self) elif format == 'spin': return to_spin_string(self) elif format == 'utf8': return to_utf8_string(self) elif format == 'lbt': return to_lbt_string(self) elif format == 'wring': return to_wring_string(self) elif format == 'latex': return to_latex_string(self) elif format == 'sclatex': return to_sclatex_string(self) else: raise ValueError("unknown string format: " + format) formula.__init__ = _formula_str_ctor formula.to_str = _formula_to_str formula.show_ast = _render_formula_as_svg def translate(formula, output='tgba', pref='small', level='high', complete=False): """Translate a formula into an automaton. Keep in mind that pref expresses just a preference that may not be satisfied. Keyword arguments: output -- the type of automaton to build ('tgba', 'ba', 'monitor') pref -- prefered characteristic of the produced automaton ('small', 'deterministic', 'any') level -- level of optimizations ('low', 'medium', 'high') complete -- whether to produce a complete automaton (True, False) """ if type(formula) == str: formula = parse_formula(formula) a = translator() if type(output) == str: output_ = output.lower() if output_ == 'tgba': output = postprocessor.TGBA elif output_ == 'ba': output = postprocessor.BA elif output_.startswith('mon'): output = postprocessor.Monitor else: raise ValueError("unknown output type: " + output) a.set_type(output) if complete: complete = postprocessor.Complete else: complete = 0 if type(pref) == str: pref_ = pref.lower() if pref_.startswith('sm'): pref = postprocessor.Small elif pref_.startswith('det'): pref = postprocessor.Deterministic elif pref_ == 'any': pref = postprocessor.Any else: raise ValueError("unknown output preference: " + pref) a.set_pref(pref | complete) if type(level) == str: level_ = level.lower() if level_ == 'high': level = postprocessor.High elif level_.starswith('med'): level = postprocessor.Medium elif level_ == 'low': level = postprocessor.Low else: raise ValueError("unknown optimization level: " + level) a.set_level(level) return a.run(formula) formula.translate = translate # 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) setattr(formulaiterator, fun, filtf) if fun[:3] == 'is_': notfun = fun[:3] + 'not_' + fun[3:] elif fun[:4] == 'has_': notfun = fun[:4] + '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() e = default_environment.instance() for elt in ap: aprops.insert(is_atomic_prop(e.require(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 def _randltlgenerator(rg): i = 0 while i != n: f = rg.next() if f is None: sys.stderr.write("Warning: could not generate a new unique formula " \ "after " + str(MAX_TRIALS) + " trials.\n") yield None else: yield f i += 1 return formulaiterator(_randltlgenerator(rg)) def simplify(f, **kwargs): level = kwargs.get('level', None) if level is not None: return ltl_simplifier(ltl_simplifier_options(level)).simplify(f) basics = kwargs.get('basics', True) synt_impl = kwargs.get('synt_impl', True) event_univ = kwargs.get('event_univ', True) containment_checks = kwargs.get('containment_checks', False) containment_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 = ltl_simplifier_options(basics, synt_impl, event_univ, containment_checks, containment_checks_stronger, nenoform_stop_on_boolean, reduce_size_strictly, boolean_to_isop, favor_event_univ) return ltl_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', 'get_literal', 'relabel', 'relabel_bse', 'simplify', 'unabbreviate_ltl']: _addmap(fun)