def test_intdata(self):
i = TimeSeries("int", "ph")
for j in range(10):
i.insert_point(j, int(j * 2.1))
self.assertEqual(len(i), 10)
self.assertEqual(i[3].ts, 3)
self.assertEqual(i[3].value, 6)
def test_dictitem(self):
i1 = EventList("test", "ph")
self.assertTrue(i1.empty())
i1.insert_point(1, dict(hey=1.0, ho=2.0))
self.assertFalse(i1.empty())
i2 = TimeSeries("test1", "ph", [(1, 1.0)])
self.assertEqual(i2[0].value, 1.0)
i2.insert_point(1, 2.0)
self.assertEqual(i2[0].value, 2.0)
i3 = TimeSeries("test2", "ph", [(1, 1.0)])
self.assertNotEqual(i2, i3)
i4 = TimeSeries("test1", "ph", [(1, 1.0)])
self.assertEqual(i2, i4)
self.assertEqual(i2.to_hash(), i4.to_hash())
def test_delete(self):
conf = get_unit_test_config()
db = Connection(engine=conf.ENGINE,
engine_options=conf.ENGINE_OPTIONS,
metric_definitions=get_test_metrics())
db.database_init(silent=True)
db.timeseries._create_metric("ph", silent=True)
base = datetime.datetime.now()
data_list = [(base - datetime.timedelta(minutes=10 * x),
random.random() * 5) for x in range(0, 144 * 5)]
ts = TimeSeries("device", "ph", values=data_list)
from_pd = pendulum.instance(data_list[-1][0])
from_ts = from_pd.int_timestamp
to_pd = pendulum.instance(data_list[0][0])
to_ts = to_pd.int_timestamp
#delete all data just in case
r = db.timeseries.delete_timeseries("device", ["ph"],
from_ts - 24 * 60 * 60,
to_ts + 24 * 60 * 60)
#insert
db.timeseries.insert_timeseries(ts)
# get
r = db.timeseries.get_single_timeseries("device", "ph", from_ts, to_ts)
a = list(r.all())
self.assertEqual(len(a), 144 * 5)
# perform delete
r = db.timeseries.delete_timeseries("device", ["ph"], from_ts, from_ts)
self.assertEqual(r, 1)
# get
r = db.timeseries.get_single_timeseries("device", "ph",
from_ts + 24 * 60 * 60,
to_ts + 24 * 60 * 60)
a = list(r.all())
self.assertEqual(len(a), 144 * 4)
# delete all
r = db.timeseries.delete_timeseries("device", ["ph"], from_ts, to_ts)
self.assertGreaterEqual(r, 5)
def test_proto(self):
res = TimeSeries("ddd", "ph")
ts = to_ts(datetime.datetime(2000, 1, 1, 0, 0))
for j in range(500):
res.insert_point(j * 600, float(j % 6))
p = FloatTimeSeries()
p.ParseFromString(res.to_proto_bytes())
self.assertEqual(len(p.timestamps), 500)
self.assertEqual(len(p.timestamp_offsets), 500)
self.assertEqual(len(p.values), 500)
self.assertEqual(p.key, "ddd")
self.assertEqual(p.metric, "ph")
ts2 = TimeSeries.from_proto(p)
self.assertEqual(len(ts2), 500)
ts3 = TimeSeries.from_proto_bytes(ts2.to_proto_bytes())
self.assertEqual(len(ts3), 500)
def test_rawitem(self):
d = []
for i in range(100):
d.append((i, i * 2.5))
self.assertEqual(len(d), 100)
d1 = list(d[:50])
d2 = list(d[50:])
random.shuffle(d1)
random.shuffle(d2)
i = TimeSeries("ph_sensor", "ph")
for t, v in d1:
i.insert_point(t, v)
i.insert(d2)
l = list([x for x in i.all()])
self.assertEqual(len(l), 100)
logging.warning(l)
for i in range(100):
self.assertEqual(l[i][0], i)
self.assertEqual(l[i][1], i * 2.5)
def test_selective_delete(self):
conf = get_unit_test_config()
db = Connection(engine=conf.ENGINE,
engine_options=conf.ENGINE_OPTIONS,
metric_definitions=get_test_metrics())
db.database_init(silent=True)
base = datetime.datetime(2019,
2,
1,
23,
50,
tzinfo=datetime.timezone.utc)
ph_data = [(base - datetime.timedelta(minutes=10 * x), ((x % 3) + 4))
for x in range(0, 144 * 5)]
act_data = [(base - datetime.timedelta(minutes=10 * x), ((x % 3) + 20))
for x in range(0, 144 * 5)]
ph = TimeSeries("dev1", "ph", values=ph_data)
act = TimeSeries("dev1", "act", values=act_data)
from_pd = pendulum.instance(ph_data[-1][0])
from_ts = from_pd.int_timestamp
to_pd = pendulum.instance(ph_data[0][0])
to_ts = to_pd.int_timestamp
#delete all data just in case
r = db.timeseries.delete_timeseries("dev1", ["act", "ph"],
from_ts - 24 * 60 * 60,
to_ts + 24 * 60 * 60)
db.timeseries.insert_timeseries(act)
db.timeseries.insert_timeseries(ph)
get_timeseries = db.timeseries.get_single_timeseries
self.assertEqual(len(get_timeseries("dev1", "ph", from_ts, to_ts)),
144 * 5)
self.assertEqual(len(get_timeseries("dev1", "act", from_ts, to_ts)),
144 * 5)
# perform delete
r = db.timeseries.delete_timeseries("dev1", ["ph"], from_ts, from_ts)
self.assertEqual(r, 1)
self.assertEqual(
len(
get_timeseries("dev1", "ph", from_ts,
from_ts + 24 * 60 * 60 - 1)), 0)
self.assertEqual(len(get_timeseries("dev1", "ph", from_ts, to_ts)),
144 * 4)
self.assertEqual(len(get_timeseries("dev1", "act", from_ts, to_ts)),
144 * 5)
delete_start = from_ts + 24 * 60 * 60
delete_end = from_ts + 24 * 60 * 60 * 3
r = db.timeseries.delete_timeseries("dev1", ["act"],
delete_start + 12 * 60 * 60,
delete_end - 12 * 60 * 60)
self.assertEqual(r, 2)
self.assertEqual(
len(
get_timeseries("dev1", "ph", from_ts,
from_ts + 24 * 60 * 60 - 1)), 0)
self.assertEqual(len(get_timeseries("dev1", "ph", from_ts, to_ts)),
144 * 4)
self.assertEqual(len(get_timeseries("dev1", "act", from_ts, to_ts)),
144 * 3)
self.assertEqual(
len(get_timeseries("dev1", "act", from_ts, delete_start)), 144)
self.assertEqual(
len(get_timeseries("dev1", "act", delete_start, delete_end - 1)),
0)
self.assertEqual(len(get_timeseries("dev1", "act", delete_end, to_ts)),
144 * 2)
def test_aggregations(self):
res = TimeSeries("ddd", "temp")
ts = to_ts(datetime.datetime(2000, 1, 1, 0, 0))
for _ in range(10):
for j in range(144):
res.insert_point(ts + j * 600, float(j % 6))
ts += 144 * 600
# All
self.assertEqual(len(list(res.all())), 144 * 10)
# Daily
daily = list(res.daily())
self.assertEqual(len(daily), 10)
self.assertEqual(len(list(daily[0])), 144)
# Daily Aggr
g = res.aggregation("daily", "sum")
for x in g:
self.assertEqual(x[1], 360.0)
g = res.aggregation("daily", "count")
for x in g:
self.assertEqual(x[1], 144)
g = res.aggregation("daily", "mean")
for x in g:
self.assertEqual(x[1], 2.5)
g = res.aggregation("daily", "min")
for x in g:
self.assertEqual(x[1], 0.0)
g = res.aggregation("daily", "max")
for x in g:
self.assertEqual(x[1], 5.0)
g = res.aggregation("daily", "amp")
for x in g:
self.assertEqual(x[1], 5.0)
# Hourly
daily = list(res.daily())
self.assertEqual(len(daily), 10)
self.assertEqual(len(list(daily[0])), 144)
# Hourly Aggr
g = res.aggregation("hourly", "sum")
for x in g:
self.assertEqual(x[1], 15.0)
g = res.aggregation("hourly", "count")
for x in g:
self.assertEqual(x[1], 6)
g = res.aggregation("hourly", "mean")
for x in g:
self.assertEqual(x[1], 2.5)
g = res.aggregation("hourly", "min")
for x in g:
self.assertEqual(x[1], 0.0)
g = res.aggregation("hourly", "max")
for x in g:
self.assertEqual(x[1], 5.0)
g = res.aggregation("hourly", "amp")
for x in g:
self.assertEqual(x[1], 5.0)
def test_local_aggregation(self):
ts1 = TimeSeries("ddd", "temp")
ts2 = TimeSeries("fff", "temp")
# daily, timeshift to winter
start = pendulum.datetime(2018, 10, 25, 0, 0, tz='Europe/Vienna')
cur = start
for i in range(10):
for j in range(24 * 6):
ts1.insert_point(cur, float(i+1))
cur = cur.add(minutes=10)
end = cur
self.assertEqual(start.add(days=10).subtract(hours=1), end)
l = [list(x) for x in ts1.daily_local()]
self.assertEqual(len(l), 10)
self.assertEqual(len(l[0]), 144)
self.assertEqual(l[0][0].dt, start)
for i in range(144):
self.assertEqual(l[0][i].value, 1.0)
self.assertEqual(l[1][0].dt, start.add(days=1))
for i in range(144):
self.assertEqual(l[1][i].value, 2.0)
l = list(ts1.aggregation("daily", "mean", raw=False, tz_mode="local"))
self.assertEqual(len(l), 10)
self.assertEqual(l[0].dt, start)
self.assertEqual(l[-1].dt, start.add(days=9))
l = list(ts1.aggregation("daily", "mean", raw=True, tz_mode="local"))
self.assertEqual(len(l), 10)
self.assertEqual(l[0].ts, start.int_timestamp)
self.assertEqual(l[0].ts_offset, 7200)
self.assertEqual(l[0].value, 1.0)
self.assertEqual(l[-1].ts, end.start_of("day").int_timestamp)
self.assertEqual(l[-1].ts_offset, 3600)
self.assertEqual(l[-1].value, 10.0)
# daily, timeshift to summer
start = pendulum.datetime(2018, 3, 20, 0, 0, tz='Europe/Vienna')
cur = start
for i in range(10):
for j in range(24 * 6):
ts2.insert_point(cur, float(i+1))
cur = cur.add(minutes=10)
end = cur
self.assertEqual(start.add(days=10).add(hours=1), end)
l = list(ts2.aggregation("daily", "all", raw=False, tz_mode="local"))
print(l)
self.assertEqual(len(l), 11)
self.assertEqual(l[0].dt, start)
self.assertEqual(l[-1].dt, start.add(days=10))
l = list(ts2.aggregation("daily", "all", raw=True, tz_mode="local"))
self.assertEqual(len(l), 11)
self.assertEqual(l[0].ts, start.int_timestamp)
self.assertEqual(l[0].ts_offset, 3600)
self.assertEqual(l[0].value.count, 144)
self.assertEqual(l[0].value.mean, 1.0)
self.assertEqual(l[0].value.stdev, 0.0)
self.assertEqual(l[5].value.count, 144 - 6)
self.assertEqual(l[-1].ts, end.start_of("day").int_timestamp)
self.assertEqual(l[-1].ts_offset, 7200)
self.assertEqual(l[-1].value.count, 6)
self.assertEqual(l[-1].value.mean, 10.0)
self.assertEqual(l[-1].value.stdev, 0.0)