123 lines
2.7 KiB
Python
123 lines
2.7 KiB
Python
# TODO: figure out how to deduplicate this with robustness
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# - Abstract as working on distributive lattice
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from functools import singledispatch
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import operator as op
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import funcy as fn
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from lenses import lens
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import stl.ast
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import stl
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oo = float('inf')
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def pointwise_sat(phi):
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ap_names = [z.id.name for z in stl.utils.AP_lens(phi).get_all()]
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def _eval_stl(x, t):
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evaluated = stl.utils.eval_lineqs(phi, x)
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evaluated.update(fn.project(x, ap_names))
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return eval_stl(phi)(evaluated, t)
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return _eval_stl
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@singledispatch
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def eval_stl(stl):
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raise NotImplementedError
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@eval_stl.register(stl.Or)
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def _(stl):
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fs = [eval_stl(arg) for arg in stl.args]
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return lambda x, t: any(f(x, t) for f in fs)
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@eval_stl.register(stl.And)
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def _(stl):
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fs = [eval_stl(arg) for arg in stl.args]
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return lambda x, t: all(f(x, t) for f in fs)
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def get_times(x, tau, lo=None, hi=None):
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domain = fn.first(x.values()).domain
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if lo is None or lo is -oo:
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lo = domain.start()
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if hi is None or hi is oo:
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hi = domain.end()
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end = min(v.domain.end() for v in x.values())
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hi = hi + tau if hi + tau <= end else end
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lo = lo + tau if lo + tau <= end else end
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if lo > hi:
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return []
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elif hi == lo:
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return [lo]
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all_times = fn.cat(v.slice(lo, hi).items() for v in x.values())
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return sorted(set(fn.pluck(0, all_times)))
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@eval_stl.register(stl.Until)
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def _(stl):
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def _until(x, t):
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f1, f2 = eval_stl(stl.arg1), eval_stl(stl.arg2)
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for tau in get_times(x, t):
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if not f1(x, tau):
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return f2(x, tau)
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return False
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return _until
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def eval_unary_temporal_op(phi, always=True):
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fold = all if always else any
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lo, hi = phi.interval
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if lo > hi:
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retval = True if always else False
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return lambda x, t: retval
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f = eval_stl(phi.arg)
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if hi == lo:
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return lambda x, t: f(x, t)
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return lambda x, t: fold(f(x, tau) for tau in get_times(x, t, lo, hi))
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@eval_stl.register(stl.F)
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def _(phi):
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return eval_unary_temporal_op(phi, always=False)
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@eval_stl.register(stl.G)
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def _(phi):
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return eval_unary_temporal_op(phi, always=True)
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@eval_stl.register(stl.Neg)
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def _(stl):
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f = eval_stl(stl.arg)
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return lambda x, t: not f(x, t)
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op_lookup = {
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">": op.gt,
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">=": op.ge,
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"<": op.lt,
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"<=": op.le,
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"=": op.eq,
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}
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@eval_stl.register(stl.AtomicPred)
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def _(stl):
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return lambda x, t: x[str(stl.id)][t]
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@eval_stl.register(stl.LinEq)
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def _(lineq):
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return lambda x, t: x[lineq][t]
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def eval_terms(lineq, x, t):
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terms = lens(lineq).terms.each_().get_all()
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return sum(eval_term(term, x, t) for term in terms)
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def eval_term(term, x, t):
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return float(term.coeff)*x[term.id.name][t]
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