continue refactoring to optimize for mtl
This commit is contained in:
Marcell Vazquez-Chanlatte
parent
5fd66cfd2c
commit
98824c9ba1
21 changed files with 393 additions and 467 deletions
279
stl/ast.py
279
stl/ast.py
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@ -1,279 +0,0 @@
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# -*- coding: utf-8 -*-
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from collections import deque, namedtuple
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from functools import lru_cache
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import funcy as fn
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from lenses import lens, bind
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import stl
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def flatten_binary(phi, op, dropT, shortT):
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def f(x):
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return x.args if isinstance(x, op) else [x]
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args = [arg for arg in phi.args if arg is not dropT]
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if any(arg is shortT for arg in args):
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return shortT
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elif not args:
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return dropT
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elif len(args) == 1:
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return args[0]
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else:
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return op(tuple(fn.mapcat(f, phi.args)))
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class AST(object):
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__slots__ = ()
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def __or__(self, other):
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return flatten_binary(Or((self, other)), Or, BOT, TOP)
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def __and__(self, other):
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return flatten_binary(And((self, other)), And, TOP, BOT)
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def __invert__(self):
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if isinstance(self, Neg):
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return self.arg
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return Neg(self)
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def __rshift__(self, t):
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if self in (BOT, TOP):
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return self
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phi = self
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for _ in range(t):
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phi = Next(phi)
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return phi
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def __call__(self, trace, time=0):
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return stl.pointwise_sat(self)(trace, time)
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@property
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def children(self):
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return tuple()
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def walk(self):
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"""Walk of the AST."""
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pop = deque.pop
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children = deque([self])
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while len(children) > 0:
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node = pop(children)
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yield node
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children.extend(node.children)
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@property
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def params(self):
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def get_params(leaf):
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if isinstance(leaf, ModalOp):
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if isinstance(leaf.interval[0], Param):
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yield leaf.interval[0]
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if isinstance(leaf.interval[1], Param):
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yield leaf.interval[1]
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return set(fn.mapcat(get_params, self.walk()))
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def set_params(self, val):
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phi = param_lens(self)
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return phi.modify(lambda x: float(val.get(x, val.get(str(x), x))))
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@property
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def atomic_predicates(self):
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return set(AP_lens.collect()(self))
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def inline_context(self, context):
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phi, phi2 = self, None
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def update(ap):
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return context.get(ap, ap)
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while phi2 != phi:
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phi2, phi = phi, AP_lens.modify(update)(phi)
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return phi
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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class _Top(AST):
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__slots__ = ()
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def __repr__(self):
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return "1"
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def __invert__(self):
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return BOT
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class _Bot(AST):
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__slots__ = ()
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def __repr__(self):
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return "0"
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def __invert__(self):
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return TOP
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TOP = _Top()
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BOT = _Bot()
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class AtomicPred(namedtuple("AP", ["id"]), AST):
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__slots__ = ()
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def __repr__(self):
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return f"{self.id}"
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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@property
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def children(self):
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return tuple()
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class Interval(namedtuple('I', ['lower', 'upper'])):
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__slots__ = ()
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def __repr__(self):
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return f"[{self.lower},{self.upper}]"
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class NaryOpSTL(namedtuple('NaryOp', ['args']), AST):
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__slots__ = ()
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OP = "?"
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def __repr__(self):
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return "(" + f" {self.OP} ".join(f"{x}" for x in self.args) + ")"
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@property
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def children(self):
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return tuple(self.args)
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class Or(NaryOpSTL):
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__slots__ = ()
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OP = "|"
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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class And(NaryOpSTL):
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__slots__ = ()
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OP = "&"
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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class ModalOp(namedtuple('ModalOp', ['interval', 'arg']), AST):
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__slots__ = ()
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OP = '?'
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def __repr__(self):
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if self.interval.lower == 0 and self.interval.upper == float('inf'):
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return f"{self.OP}{self.arg}"
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return f"{self.OP}{self.interval}{self.arg}"
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@property
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def children(self):
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return (self.arg,)
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class F(ModalOp):
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__slots__ = ()
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OP = "< >"
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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class G(ModalOp):
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__slots__ = ()
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OP = "[ ]"
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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class Until(namedtuple('ModalOp', ['arg1', 'arg2']), AST):
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__slots__ = ()
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def __repr__(self):
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return f"({self.arg1} U {self.arg2})"
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@property
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def children(self):
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return (self.arg1, self.arg2)
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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class Neg(namedtuple('Neg', ['arg']), AST):
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__slots__ = ()
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def __repr__(self):
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return f"~{self.arg}"
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@property
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def children(self):
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return (self.arg,)
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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class Next(namedtuple('Next', ['arg']), AST):
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__slots__ = ()
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def __repr__(self):
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return f"@{self.arg}"
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@property
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def children(self):
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return (self.arg,)
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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class Param(namedtuple('Param', ['name']), AST):
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__slots__ = ()
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def __repr__(self):
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return self.name
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def __hash__(self):
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# TODO: compute hash based on contents
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return hash(repr(self))
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@lru_cache()
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def param_lens(phi, *, getter=False):
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return bind(phi).Recur(Param)
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def type_pred(*args):
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ast_types = set(args)
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return lambda x: type(x) in ast_types
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AP_lens = lens.Recur(AtomicPred)
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