def test_merge():
ts_a = TimeSeries()
ts_b = TimeSeries()
# ts_a[0] = None
ts_b[0] = True
ts_merge = TimeSeries.merge([ts_a, ts_b])
assert True in ts_merge[0]
assert None in ts_merge[0]
ts_c = TimeSeries.merge([])
assert list(ts_c.items()) == []
def test_single_merges():
# a single empty time series
ts = TimeSeries()
assert TimeSeries.merge([ts]) == TimeSeries(default=[None])
# multiple empty time series
ts_a = TimeSeries()
ts_b = TimeSeries()
assert TimeSeries.merge([ts_a, ts_b]) == \
TimeSeries(default=[None, None])
# test a single time series with only one measurement
ts = TimeSeries()
ts[21] = 42
merged = TimeSeries.merge([ts])
assert list(merged.items()) == [(21, [42])]
# test an empty time series and a time series with one measurement
ts_a = TimeSeries()
ts_a[21] = 42
ts_b = TimeSeries()
ts = TimeSeries.merge([ts_a, ts_b])
assert ts[20] == [None, None]
assert ts[23] == [42, None]
# test an empty time series and a time series with one entry
ts_a = TimeSeries()
ts_a[21] = 42
ts_a[22] = 41
ts_a[23] = 40
ts_b = TimeSeries()
ts_b[20] = 1
ts_b[22] = 2
ts_b[24] = 3
merged = TimeSeries.merge([ts_a, ts_b])
assert list(merged.items()) == [
(20, [None, 1]),
(21, [42, 1]),
(22, [41, 2]),
(23, [40, 2]),
(24, [40, 3]),
]
def test_single_merges():
# a single empty time series
ts = TimeSeries()
nose.tools.assert_raises(ValueError, TimeSeries.merge, [ts])
# multiple empty time series
ts_a = TimeSeries()
ts_b = TimeSeries()
nose.tools.assert_raises(ValueError, TimeSeries.merge, [ts_a, ts_b])
# test a single time series with only one measurement
ts = TimeSeries()
ts[21] = 42
merged = TimeSeries.merge([ts])
assert merged.items() == [(21, [42])]
# test an empty time series and a time series with one measurement
ts_a = TimeSeries()
ts_a[21] = 42
ts_b = TimeSeries()
nose.tools.assert_raises(ValueError, TimeSeries.merge, [ts_a, ts_b])
# test an empty time series and a time series with one entry
ts_a = TimeSeries()
ts_a[21] = 42
ts_a[22] = 41
ts_a[23] = 40
ts_b = TimeSeries()
ts_b[20] = 1
ts_b[22] = 2
ts_b[24] = 3
merged = TimeSeries.merge([ts_a, ts_b])
assert merged.items() == [
(20, [42, 1]),
(22, [41, 2]),
(23, [40, 2]),
(24, [40, 3]),
]
def test_merge():
ts_a = TimeSeries(default=None)
ts_b = TimeSeries(default=None)
ts_a[0] = None
ts_b[0] = True
ts_merge = TimeSeries.merge([ts_a, ts_b])
assert True in ts_merge[0]
assert None in ts_merge[0]
def group_total_beh(self):
"""
Returns: float of group total behavior index as a fraction of interval
"""
if len(self.ts_list) > 1:
ts = TimeSeries()
ts = ts.merge(self.ts_list)
return self.beh_index(ts)
else:
return self.individual_total_beh()
def test_merge():
# since this is random, do it a bunch of times
for n_trial in range(1000):
# make a list of TimeSeries that is anywhere from 0 to 5
# long. Each TimeSeries is of random length between 0 and 20,
# with random time points and random values.
ts_list = []
for i in range(random.randint(1, 5)):
ts_list.append(make_random_timeseries())
method_a = list(TimeSeries.merge(ts_list, compact=False))
method_b = list(TimeSeries.iter_merge(ts_list))
msg = '%s != %s' % (pprint.pformat(method_a), pprint.pformat(method_b))
assert method_a == method_b, msg
def example_sum():
a = TimeSeries()
a.set(datetime.datetime(2015, 3, 1), 1)
a.set(datetime.datetime(2015, 3, 2), 0)
a.set(datetime.datetime(2015, 3, 3), 1)
a.set(datetime.datetime(2015, 3, 5), 0)
a.set(datetime.datetime(2015, 3, 6), 0)
b = TimeSeries()
b.set(datetime.datetime(2015, 3, 1), 0)
b.set(datetime.datetime(2015, 3, 2, 12), 1)
b.set(datetime.datetime(2015, 3, 3, 13, 13), 0)
b.set(datetime.datetime(2015, 3, 4), 1)
b.set(datetime.datetime(2015, 3, 5), 0)
b.set(datetime.datetime(2015, 3, 5, 12), 1)
b.set(datetime.datetime(2015, 3, 5, 19), 0)
c = TimeSeries()
c.set(datetime.datetime(2015, 3, 1, 17), 0)
c.set(datetime.datetime(2015, 3, 1, 21), 1)
c.set(datetime.datetime(2015, 3, 2, 13, 13), 0)
c.set(datetime.datetime(2015, 3, 4, 18), 1)
c.set(datetime.datetime(2015, 3, 5, 4), 0)
# output the three time series
for i, ts in enumerate([a, b, c]):
for (t0, v0), (t1, v1) in ts.iterintervals(1):
print(t0.isoformat(), i)
print(t1.isoformat(), i)
print('')
for (t0, v0), (t1, v1) in ts.iterintervals(0):
print(t0.isoformat(), i)
print(t1.isoformat(), i)
print('')
# output the sum
# for dt, i in sum([a, b, c]):
# print dt.isoformat(), i
# print ''
for dt, i in TimeSeries.merge([a, b, c], operation=sum):
print(dt.isoformat(), i)
def collect_states(
trace: Trace, conditions: Iterable[SafetyCondition]
) -> Iterator[tuple[float, set[tuple[str, Any]]]]:
atoms = set(domain.keys()) | set(
Monitor(frozenset(c.condition for c in conditions)).atoms()
)
event_keys: list[_Atom] = sorted(atoms, key=lambda d: d.id)
events: TimeSeries = TimeSeries.merge([trace.values[e] for e in event_keys])
events.compact()
t: float
for t, v in events.items():
s = set()
for e, i in zip(event_keys, v):
if e in domain:
d = domain[e]
s.add((e.id, d.value(i)))
else:
s.add((e.id, i))
yield t, s
def test_sum():
a = TimeSeries()
a.set(datetime.datetime(2015, 3, 1), 1)
a.set(datetime.datetime(2015, 3, 2), 0)
a.set(datetime.datetime(2015, 3, 3), 1)
a.set(datetime.datetime(2015, 3, 4), 0)
b = TimeSeries()
b.set(datetime.datetime(2015, 3, 1), 0)
b.set(datetime.datetime(2015, 3, 1, 12), 1)
b.set(datetime.datetime(2015, 3, 2), 0)
b.set(datetime.datetime(2015, 3, 2, 12), 1)
b.set(datetime.datetime(2015, 3, 3), 0)
c = TimeSeries()
c.set(datetime.datetime(2015, 3, 1), 0)
c.set(datetime.datetime(2015, 3, 1, 18), 1)
c.set(datetime.datetime(2015, 3, 5), 0)
ts_sum = TimeSeries.merge([a, b, c], operation=ignorant(sum))
# test before domain, should give default value
assert ts_sum[datetime.datetime(2015, 2, 24)] == 0
# test values throughout sum
assert ts_sum[datetime.datetime(2015, 3, 1)] == 1
assert ts_sum[datetime.datetime(2015, 3, 1, 6)] == 1
assert ts_sum[datetime.datetime(2015, 3, 1, 12)] == 2
assert ts_sum[datetime.datetime(2015, 3, 1, 13)] == 2
assert ts_sum[datetime.datetime(2015, 3, 1, 17)] == 2
assert ts_sum[datetime.datetime(2015, 3, 1, 18)] == 3
assert ts_sum[datetime.datetime(2015, 3, 1, 19)] == 3
assert ts_sum[datetime.datetime(2015, 3, 3)] == 2
assert ts_sum[datetime.datetime(2015, 3, 4)] == 1
assert ts_sum[datetime.datetime(2015, 3, 4, 18)] == 1
assert ts_sum[datetime.datetime(2015, 3, 5)] == 0
# test after domain, should give last value
assert ts_sum[datetime.datetime(2015, 3, 6)] == 0
assert 0 + a + b == a + b
def register(self, trace: Trace):
"""Record the consecutive states encountered in the trace"""
# FIXME Key sorting relies on id field being present, not the case for non Atom keys
event_keys = sorted(
self.domain,
key=lambda d: getattr(d, "id", (str(d), )),
)
events = TimeSeries.merge(
[trace.values[getattr(e, "id", e)] for e in event_keys])
events.compact()
for _, v in events.items():
entry = set()
for (e, d), i in zip(
sorted(
self.domain.items(),
key=lambda d: getattr(d[0], "id", (str(d[0]), )),
),
v,
):
entry.add((e, d.value(i)))
self.combinations.add(frozenset(entry))
def collect_predicates(trace: Trace):
# -> Iterator[tuple[float, set[tuple[str, bool]]]]:
predicate_keys = sorted(predicates, key=lambda d: str(d))
predicate_values = list()
for predicate in predicate_keys:
v = Monitor().evaluate(
trace,
predicate,
dt=0.01,
quantitative=True,
logic=mtfl.connective.zadeh,
time=None,
)
v = [(t, c >= mtfl.connective.zadeh.const_true) for t, c in v]
ts: TimeSeries = TimeSeries(v)
ts.compact()
predicate_values.append(ts)
#
events: TimeSeries = TimeSeries.merge(predicate_values)
for t, v in events.items():
s = set()
for e, i in zip(predicate_keys, v):
s.add((e, i))
yield t, s
def update(self, other: Trace) -> Trace:
"""Update the values of the current trace with the other"""
for t, s in other.values.items():
c = self.values.get(t, TimeSeries())
self.values[t] = TimeSeries.merge([c, s], operation=self._merge_values)
return self
