def test_params1(a, b, c):
phi = mtfl.parse('G[a, b] x')
assert {x.name for x in phi.params} == {'a', 'b'}
phi2 = phi[{'a': a, 'b': b}]
assert phi2.params == set()
assert phi2 == mtfl.parse(f'G[{a}, {b}](x)')
def test_fuzzy_binop(con, a: float, b: float, c: float):
x = prepare_values(a, b, c)
assert mtfl.parse("(apa & apb)")(x, 0, logic=con) == con.tnorm([a, b])
assert mtfl.parse("(apa & apb & apc)")(x, 0, logic=con) \
== con.tnorm([a, b, c])
assert mtfl.parse("(apa | apb)")(x, 0, logic=con) == con.tconorm([a, b])
assert mtfl.parse("(apa | apb | apc)")(x, 0, logic=con) \
== con.tconorm([a, b, c])
def test_eval_regression_next_neg():
"""From issue #219"""
d = {"a": [(0, False), (1, True)]}
f = mtfl.parse("(a & (X (~a)))")
v = f(d, quantitative=False, dt=1, time=None)
assert not f(d, quantitative=False, dt=1)
assert min(t for t, _ in v) >= 0
def test_eval_regression_until_start():
"""From issue #221"""
x = {
"ap1": [(0, True), (0.1, True), (0.2, False)],
}
phi = (mtfl.parse("(X TRUE W X TRUE)"))
phi(x, 0, quantitative=False)
def test_eval_with_signal():
spec = mtfl.parse('F(above_three)')
raw_data = signal([(0, 1), (1, 2), (2, 3)], start=0, end=10, tag='a')
processed = raw_data.map(lambda val: val['a'] > 3, tag="above_three")
assert not spec(processed, quantitative=False)
assert spec(processed, quantitative=True) == 0
def test_scope():
dt = 0.3
phi = mtfl.parse('@ap1')
assert utils.scope(phi, dt) == 0.3
phi = mtfl.parse('(@@ap1 | ap2)')
assert utils.scope(phi, dt) == 0.6
phi = mtfl.parse('G[0.3, 1.2] F[0.6, 1.5] ap1')
assert utils.scope(phi, dt) == 1.2 + 1.5
phi = mtfl.parse('G[0.3, 1.2] F ap1')
assert utils.scope(phi, dt) == float('inf')
phi = mtfl.parse('G[0.3, 1.2] (ap1 U ap2)')
assert utils.scope(phi, dt) == float('inf')
def test_eval_regression_smoke1():
"""From issue #215"""
d2 = {
'a': [(0, True), (1, True), (3, True), (4, True), (5, False),
(6, True)],
'b': [(0, False), (3, True)],
}
f2 = mtfl.parse('(a U[0,3] b)')
f2(d2, quantitative=False)
def test_eval_comparison():
a = mtfl.parse("a")
b = mtfl.parse("b")
d = {
"a": [(0, 5.), (1, 10.), (3, 0.), (4, 10.)],
"b": [(0, 15.), (2, 5.), (4, 10.)],
}
i = a < b
assert i(d, time=0, quantitative=False)
assert i(d, time=1, quantitative=False)
assert not i(d, time=2, quantitative=False)
assert i(d, time=3, quantitative=False)
assert not i(d, time=4, quantitative=False)
i = a.eq(b)
assert not i(d, time=0, quantitative=False)
assert i(d, time=4, quantitative=False)
def test_eval_regression_timed_until():
"""From issue #217"""
x = {
'start': [(0, True), (200, False)],
'success': [(0, False), (300, True)]
}
phi = mtfl.parse('(~start U[0,120] success)')
assert phi(x, time=200, quantitative=False, dt=1)
y = {
'start': [(0, True), (1, False), (5, True), (6, True)],
'success': [(0, False), (20, True)]
}
phi1 = mtfl.parse('(start U[0,20] success)')
assert phi1(y, time=6, quantitative=False, dt=1)
z = {
'start': [(0, True), (200, False)],
'success': [(0, False), (300, True)]
}
phi2 = mtfl.parse('F[0,120]success')
assert phi2(z, time=181, quantitative=False, dt=1)
def test_eval_smoke_tests(phi):
assert mtfl.parse('~ap4')(x, 0, quantitative=False)
assert mtfl.parse('G[0.1, 0.03] ~ap4')(x, 0, quantitative=False)
phi6 = mtfl.parse('G[0.1, 0.03] ~ap5')
assert phi6(x, 0, quantitative=False)
assert phi6(x, 0.2, quantitative=False)
assert mtfl.parse('G ~ap4')(x, 0, quantitative=False)
assert mtfl.parse('F ap5')(x, 0, quantitative=False)
assert mtfl.parse('(ap1 U ap2)')(x, 0, quantitative=False)
assert not mtfl.parse('(ap2 U ap2)')(x, 0, quantitative=False)
def test_discretize():
dt = 0.3
phi = mtfl.parse('@ ap1')
assert utils.is_discretizable(phi, dt)
phi2 = utils.discretize(phi, dt)
phi3 = utils.discretize(phi2, dt)
assert phi2 == phi3
phi = mtfl.parse('G[0.3, 1.2] F[0.6, 1.5] ap1')
assert utils.is_discretizable(phi, dt)
phi2 = utils.discretize(phi, dt)
phi3 = utils.discretize(phi2, dt)
assert phi2 == phi3
phi = mtfl.parse('G[0.3, 1.4] F[0.6, 1.5] ap1')
assert not utils.is_discretizable(phi, dt)
phi = mtfl.parse('G[0.3, 1.2] F ap1')
assert not utils.is_discretizable(phi, dt)
phi = mtfl.parse('G[0.3, 1.2] (ap1 U ap2)')
assert not utils.is_discretizable(phi, dt)
phi = mtfl.parse('G[0.3, 0.6] ~F[0, 0.3] a')
assert utils.is_discretizable(phi, dt)
phi2 = utils.discretize(phi, dt, distribute=True)
phi3 = utils.discretize(phi2, dt, distribute=True)
assert phi2 == phi3
phi = mtfl.TOP
assert utils.is_discretizable(phi, dt)
phi2 = utils.discretize(phi, dt)
phi3 = utils.discretize(phi2, dt)
assert phi2 == phi3
phi = mtfl.BOT
assert utils.is_discretizable(phi, dt)
phi2 = utils.discretize(phi, dt)
phi3 = utils.discretize(phi2, dt)
assert phi2 == phi3
def test_walk():
phi = mtfl.parse('(([ ][0, 1] ap1 & < >[1,2] ap2) | (@ap1 U ap2))')
assert len(list((~phi).walk())) == 18
def test_stablizing_repr(phi):
for _ in range(10):
phi, phi2 = mtfl.parse(str(phi)), phi
assert phi == phi2
def test_output(self):
P = World()
w = Monitor()
print("== Predicates test =====")
w += ~P.operator.has_component
w += P.operator.has_component
w += ~P.operator.has_component
w += P.operator.height.eq(5)
w += P.operator.height < 42
w += P.operator.height <= 42
w += P.operator.height > 42
w += P.operator.height >= 42
w += P.operator.height.eq(P.constraint.maximum_height)
w += P.operator.height < P.constraint.maximum_height
w += (P.operator.height > 5) & (P.operator.height <
P.constraint.maximum_height)
assert len({P.operator.height, P.operator.height}) == 1
assert len({P.operator.height.eq(5), P.operator.height.eq(5)}) == 1
pprint(w.atoms())
print("== Trace insertion test =====")
t = Trace()
t[P.constraint.maximum_height] = (0, 30)
t[P.operator.height] = (5, 42)
t[P.operator.height] = (6, 5)
t[P.operator.height] = (7, 30)
t[P.constraint.maximum_height] = (8, 40)
with pytest.raises(Exception):
t[P.unknown] = (9, True)
pprint({s: list(v.items()) for s, v in t.values.items()})
print("== Trace eval test =====")
print("-- Dummy -----")
p = mtfl.parse("dummy_predicate")
print(p)
print(w.evaluate(t, p))
print("-- Op Height == 5 -----")
print((P.operator.height.eq(5)))
print(t.project({P.operator.height}))
print(w.evaluate(t, P.operator.height.eq(5), time=None))
print("== Enum predicates =====")
class DummyEnum(enum.Enum):
FOO = enum.auto()
BAR = enum.auto()
BAZ = enum.auto()
n = ~(P.operator.position.eq(DummyEnum.FOO))
f = P.operator.position.eq(DummyEnum.FOO)
t[P.operator.position] = (0, DummyEnum.FOO)
t[P.operator.position] = (10, DummyEnum.BAR)
print(n)
print(w.evaluate(t, n, time=None))
print(f)
print(w.evaluate(t, f, time=None))
print("== Test comparisons =====")
i = Monitor()
j = Monitor()
i += P.props.height.eq(5)
j += P.props.height.eq(5)
assert i == j
i += P.props.height < 5
assert i != j
print(hash(i), i)
print(hash(j), j)
print("== Test merging =====")
i = Monitor()
j = Monitor()
i += P.height.eq(5)
j += P.height < 5
print(i, j)
assert (j | i).atoms() == j.atoms()
j += P.height <= 5
assert (j | i).atoms() == i.atoms()
i += P.speed >= 4
assert (j | i).atoms() == i.atoms()
print("== Merge =====")
u = Monitor()
u += P.operator.height > 180
v = Monitor()
v += P.operator.position.eq(DummyEnum.FOO)
w = Monitor()
w += P.operator.position.eq(DummyEnum.FOO)
w += P.operator.height < 170
print(w.atoms())
print((v | w).atoms())
print((w | v).atoms())
print((u | v).atoms())
s = Trace()
s[P.operator.height] = (0, 170)
s[P.operator.height] = (1, 160)
print("s:", {n: list(v) for n, v in s.values.items()})
t = Trace()
t[P.position] = (0, DummyEnum.BAR)
t[P.operator.height] = (0, 210)
t[P.position] = (5, DummyEnum.BAZ)
t[P.position] = (9, DummyEnum.FOO)
print("t:", t.values)
print("s|t:", list((s | t).values[P.operator.height].items()))
print("t|s:", list((t | s).values[P.operator.height].items()))
print("== Out-of-order updates =====")
w = Monitor()
w += P.operator.height < 170
t = Trace()
t[P.operator.height] = (0, 210)
t[P.operator.height] = (10, 160)
t[P.operator.height] = (50, 200)
t[P.operator.height] = (25, 42)
t[P.operator.height] = (5, 10)
print(list(t.values[P.operator.height].items()))
print("== Overlapping updates =====")
t = Trace()
t[P.operator.height] = (0, 210)
t[P.operator.height] = (5, 10)
t[P.operator.height] = (10, 160)
t[P.operator.height] = (25, 42)
t[P.operator.height] = (50, 200)
print(list(t.values[P.operator.height].items()))
t[P.height] = (0, 100)
t[P.height] = (11, 161)
t[P.height] = (49, 40)
t[P.height] = (50, 150)
print(list(t.values[P.height].items()))
print(t)
def test_sugar_smoke():
mtfl.parse('(x <-> x)')
mtfl.parse('(x -> x)')
mtfl.parse('(x ^ x)')
def test_fuzzy_neg(con, a: float, b: float, c: float):
x = prepare_values(a, b, c)
assert mtfl.parse("~ap1")(x, 0, logic=con) == con.negation(a)
def test_fuzzy_implies(con, a: float, b: float, c: float):
x = prepare_values(a, b, c)
assert mtfl.parse("(apa -> apb)")(x, 0, logic=con) == con.implication(a, b)
def test_fuzzy_always(con, a: float, b: float, c: float):
x = prepare_values(a, b, c)
assert mtfl.parse("G ap1")(x, 0, logic=con) == con.tnorm([a, b, c])
import mtfl
from mtfl import utils
from mtfl import sugar
from mtfl.hypothesis import MetricTemporalLogicStrategy
from hypothesis import given
CONTEXT = {
'ap1': mtfl.parse('x'),
'ap2': mtfl.parse('(y U z)'),
'ap3': mtfl.parse('x'),
'ap4': mtfl.parse('(x -> y -> z)'),
'ap5': mtfl.parse('(ap1 <-> y <-> z)'),
}
APS = set(CONTEXT.keys())
def test_inline_context_rigid():
phi = mtfl.parse('G ap1')
assert phi[CONTEXT] == mtfl.parse('G x')
phi = mtfl.parse('G ap5')
assert phi[CONTEXT] == mtfl.parse('G(x <-> y <-> z)')
@given(MetricTemporalLogicStrategy)
def test_inline_context(phi):
assert not (APS & phi[CONTEXT].atomic_predicates)
@given(MetricTemporalLogicStrategy, MetricTemporalLogicStrategy)
import hypothesis.strategies as st
from hypothesis_cfg import ContextFreeGrammarStrategy
import mtfl
GRAMMAR = {
'phi': (('Unary', 'phi'), ('(', 'phi', 'Binary', 'phi', ')'), ('AP', ),
('FALSE', ), ('TRUE', )),
'Unary': (('~', ), ('G', 'Interval'), ('F', 'Interval'), ('X', )),
'Interval': (('', ), ('[1, 3]', )),
'Binary': (
(' | ', ),
(' & ', ),
(' -> ', ),
(' <-> ', ),
(' ^ ', ),
(' U ', ),
),
'AP': (('ap1', ), ('ap2', ), ('ap3', ), ('ap4', ), ('ap5', )),
}
MetricTemporalLogicStrategy = st.builds(
lambda term: mtfl.parse(''.join(term)),
ContextFreeGrammarStrategy(GRAMMAR, max_length=14, start='phi'))
def test_inline_context_rigid():
phi = mtfl.parse('G ap1')
assert phi[CONTEXT] == mtfl.parse('G x')
phi = mtfl.parse('G ap5')
assert phi[CONTEXT] == mtfl.parse('G(x <-> y <-> z)')
