def test_transform_rolling(self):
ts = carbonara.TimeSerie.from_tuples([
(datetime64(2014, 1, 1, 12, 0, 0), 1),
(datetime64(2014, 1, 1, 12, 1, 0), 3),
(datetime64(2014, 1, 1, 12, 2, 0), 5),
(datetime64(2014, 1, 1, 12, 3, 0), 7),
(datetime64(2014, 1, 1, 12, 4, 0), 9)
])
ts = carbonara.AggregatedTimeSerie.from_timeseries(
[ts], sampling=60, aggregation_method="last")
ts1 = ts.transform(
[carbonara.Transformation("rolling", ('mean', '2'))])
self.assertEqual(4, len(ts1))
self.assertEqual([2.0, 4.0, 6.0, 8.0], [
ts1[datetime64(2014, 1, 1, 12, 1, 0)][1], ts1[datetime64(
2014, 1, 1, 12, 2, 0)][1], ts1[datetime64(
2014, 1, 1, 12, 3, 0)][1], ts1[datetime64(
2014, 1, 1, 12, 4, 0)][1]
])
ts1 = ts.transform([carbonara.Transformation("rolling", ('sum', '4'))])
self.assertEqual(2, len(ts1))
self.assertEqual([16.0, 24.0], [
ts1[datetime64(2014, 1, 1, 12, 3, 0)][1], ts1[datetime64(
2014, 1, 1, 12, 4, 0)][1]
])
self.assertRaises(carbonara.TransformError, ts.transform,
[carbonara.Transformation("rolling", ('sum', 0))])
self.assertRaises(carbonara.TransformError, ts.transform,
[carbonara.Transformation("rolling", ('sum', 10))])
def test_unknown_transform(self):
ts = carbonara.TimeSerie.from_tuples([
(datetime64(2014, 1, 1, 12, 0, 0), -3),
(datetime64(2014, 1, 1, 12, 1, 0), 5),
(datetime64(2014, 1, 1, 12, 2, 0), -6)
])
ts = carbonara.AggregatedTimeSerie.from_timeseries(
[ts], sampling=60, aggregation_method="last")
self.assertRaises(carbonara.TransformError, ts.transform,
[carbonara.Transformation("rubbish", tuple())])
def test_resample_no_metric(self):
"""https://github.com/gnocchixyz/gnocchi/issues/69"""
transform = [
carbonara.Transformation("resample", (numpy.timedelta64(1, 'h'), ))
]
self.assertEqual([],
self.storage.get_measures(
self.metric,
datetime64(2014, 1, 1),
datetime64(2015, 1, 1),
granularity=numpy.timedelta64(300, 's'),
transform=transform))
def test_transform(self):
ts = carbonara.TimeSerie.from_tuples([
(datetime64(2014, 1, 1, 12, 0, 0), -3),
(datetime64(2014, 1, 1, 12, 1, 0), 5),
(datetime64(2014, 1, 1, 12, 2, 0), -6)
])
ts = carbonara.AggregatedTimeSerie.from_timeseries(
[ts], sampling=60, aggregation_method="last")
ts = ts.transform([carbonara.Transformation("absolute", tuple())])
self.assertEqual(3, len(ts))
self.assertEqual([3, 5, 6], [
ts[datetime64(2014, 1, 1, 12, 0, 0)][1], ts[datetime64(
2014, 1, 1, 12, 1, 0)][1], ts[datetime64(2014, 1, 1, 12, 2,
0)][1]
])
ts = ts.transform([
carbonara.Transformation("absolute", tuple()),
carbonara.Transformation("negative", tuple())
])
self.assertEqual(3, len(ts))
self.assertEqual([-3, -5, -6], [
ts[datetime64(2014, 1, 1, 12, 0, 0)][1], ts[datetime64(
2014, 1, 1, 12, 1, 0)][1], ts[datetime64(2014, 1, 1, 12, 2,
0)][1]
])
ts = ts.transform([
carbonara.Transformation("absolute", tuple()),
carbonara.Transformation("resample",
(numpy.timedelta64(360, 's'), ))
])
self.assertEqual(1, len(ts))
self.assertEqual(6, ts[datetime64(2014, 1, 1, 12, 0, 0)][1])
def transform(name, *args):
parser = pp.Keyword(name)
args_parser = pp.Suppress("(").setName("(")
first = True
for arg in args:
if not first:
args_parser += pp.Suppress(",").setName(",")
args_parser += arg
first = False
args_parser += pp.Suppress(")").setName(")")
if not args:
args_parser = pp.Optional(args_parser)
parser = parser + pp.Group(args_parser)
return parser.setParseAction(
lambda t: carbonara.Transformation(t[0], tuple(t[1])))
def test_resample(self):
ts = carbonara.TimeSerie.from_data([
datetime64(2014, 1, 1, 12, 0, 0),
datetime64(2014, 1, 1, 12, 0, 4),
datetime64(2014, 1, 1, 12, 0, 9),
datetime64(2014, 1, 1, 12, 0, 11),
datetime64(2014, 1, 1, 12, 0, 12)
], [3, 5, 6, 2, 4])
agg_ts = self._resample(ts, numpy.timedelta64(5, 's'), 'mean')
self.assertEqual(3, len(agg_ts))
agg_ts = agg_ts.resample(numpy.timedelta64(10, 's'))
self.assertEqual(2, len(agg_ts))
self.assertEqual(5, agg_ts[0][1])
self.assertEqual(3, agg_ts[1][1])
agg_ts = agg_ts.transform([
carbonara.Transformation("resample",
(numpy.timedelta64(10, 's'), ))
])
self.assertEqual(2, len(agg_ts))
self.assertEqual(5, agg_ts[0][1])
self.assertEqual(3, agg_ts[1][1])