158 lines
3.5 KiB
Python
158 lines
3.5 KiB
Python
# -*- coding: utf-8 -*-
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# TODO: create iso lens between sugar and non-sugar
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# TODO: supress + given a + (-b). i.e. want a - b
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from collections import namedtuple, deque
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from itertools import repeat
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from enum import Enum
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import funcy as fn
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from sympy import Symbol
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dt_sym = Symbol('dt', positive=True)
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t_sym = Symbol('t', positive=True)
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def flatten_binary(phi, op, dropT, shortT):
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f = lambda x: x.args if isinstance(x, op) else [x]
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args = [arg for arg in phi.args if not isinstance(arg, type(dropT))]
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if any(isinstance(arg, type(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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else:
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return op(tuple(fn.mapcat(f, phi.args)))
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class AST(object):
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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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return Neg(self)
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class _Top(AST):
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def __repr__(self):
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return "⊤"
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def __invert__(self):
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return Bot()
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class _Bot(AST):
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def __repr__(self):
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return "⊥"
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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", "time"]), AST):
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def __repr__(self):
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return f"{self.id}[{self.time}]"
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def children(self):
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return []
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class LinEq(namedtuple("LinEquality", ["terms", "op", "const"]), AST):
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def __repr__(self):
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return " + ".join(map(str, self.terms)) + f" {self.op} {self.const}"
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def children(self):
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return []
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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 Var(namedtuple("Var", ["coeff", "id", "time"])):
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def __repr__(self):
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return f"{self.coeff}*{self.id}[{self.time}]"
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class Interval(namedtuple('I', ['lower', 'upper'])):
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def __repr__(self):
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return f"[{self.lower},{self.upper}]"
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def children(self):
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return [self.lower, self.upper]
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class NaryOpSTL(namedtuple('NaryOp', ['args']), AST):
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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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def children(self):
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return self.args
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class Or(NaryOpSTL):
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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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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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def __repr__(self):
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return f"{self.OP}{self.interval}({self.arg})"
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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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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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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', ['interval', 'arg1', 'arg2']), AST):
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def __repr__(self):
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return f"({self.arg1} U{self.interval} ({self.arg2}))"
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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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def __repr__(self):
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return f"¬({self.arg})"
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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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