def test_null_zero_sum(self):
s = TimeSeries("s", 0, 1, 1, [None])
s.pathExpression = 's'
[series] = functions.sumSeries({}, [s])
self.assertEqual(list(series), [None])
s = TimeSeries("s", 0, 1, 1, [None, 1])
s.pathExpression = 's'
t = TimeSeries("s", 0, 1, 1, [None, None])
t.pathExpression = 't'
[series] = functions.sumSeries({}, [s, t])
self.assertEqual(list(series), [None, 1])
def test_null_zero_sum(self):
s = TimeSeries("s", 0, 1, 1, [None])
s.pathExpression = 's'
[series] = functions.sumSeries({}, [s])
self.assertEqual(list(series), [None])
s = TimeSeries("s", 0, 1, 1, [None, 1])
s.pathExpression = 's'
t = TimeSeries("s", 0, 1, 1, [None, None])
t.pathExpression = 't'
[series] = functions.sumSeries({}, [s, t])
self.assertEqual(list(series), [None, 1])
def test_holt_winters(self):
timespan = 3600 * 24 * 8 # 8 days
stop = int(time.time())
step = 100
series = TimeSeries('foo.bar',
stop - timespan,
stop,
step,
[x**1.5 for x in range(0, timespan, step)])
series[10] = None
series.pathExpression = 'foo.bar'
self.write_series(series, [(100, timespan)])
ctx = {
'startTime': parseATTime('-1d'),
}
analysis = functions.holtWintersForecast(ctx, [series])
self.assertEqual(len(analysis), 1)
analysis = functions.holtWintersConfidenceBands(ctx, [series])
self.assertEqual(len(analysis), 2)
analysis = functions.holtWintersConfidenceArea(ctx, [series])
self.assertEqual(len(analysis), 2)
analysis = functions.holtWintersAberration(ctx, [series])
self.assertEqual(len(analysis), 1)
def _generate_series_list(self):
seriesList = []
config = [range(101), range(101), [1] + [None] * 100]
for i, c in enumerate(config):
name = "collectd.test-db{0}.load.value".format(i + 1)
series = TimeSeries(name, 0, 101, 1, c)
series.pathExpression = name
seriesList.append(series)
return seriesList
def _generate_series_list(self, config=(range(101), range(2, 103), [1] * 2 + [None] * 90 + [1] * 2 + [None] * 7)):
seriesList = []
now = int(time.time())
for i, c in enumerate(config):
name = "collectd.test-db{0}.load.value".format(i + 1)
series = TimeSeries(name, now - 101, now, 1, c)
series.pathExpression = name
seriesList.append(series)
return seriesList
def _generate_series_list(self,
config=(range(101), range(2, 103), [1] * 2 +
[None] * 90 + [1] * 2 + [None] * 7)):
seriesList = []
now = int(time.time())
for i, c in enumerate(config):
name = "collectd.test-db{0}.load.value".format(i + 1)
series = TimeSeries(name, now - 101, now, 1, c)
series.pathExpression = name
seriesList.append(series)
return seriesList
def test_time_stack(self):
timespan = 3600 * 24 * 8 # 8 days
stop = int(time.time())
step = 100
series = TimeSeries("foo.bar", stop - timespan, stop, step, [x ** 1.5 for x in range(0, timespan, step)])
series[10] = None
series.pathExpression = "foo.bar"
self.write_series(series, [(100, timespan)])
ctx = {"startTime": parseATTime("-1d"), "endTime": parseATTime("now")}
stack = functions.timeStack(ctx, [series], "1d", 0, 7)
self.assertEqual(len(stack), 7)
stack = functions.timeStack(ctx, [series], "-1d", 0, 7)
self.assertEqual(len(stack), 7)
def test_time_stack(self):
timespan = 3600 * 24 * 8 # 8 days
stop = int(time.time())
step = 100
series = TimeSeries('foo.bar', stop - timespan, stop, step,
[x**1.5 for x in range(0, timespan, step)])
series[10] = None
series.pathExpression = 'foo.bar'
self.write_series(series, [(100, timespan)])
ctx = {'startTime': parseATTime('-1d'), 'endTime': parseATTime('now')}
stack = functions.timeStack(ctx, [series], '1d', 0, 7)
self.assertEqual(len(stack), 7)
stack = functions.timeStack(ctx, [series], '-1d', 0, 7)
self.assertEqual(len(stack), 7)
def mostChange(requestContext, seriesList):
"""
Takes one metric or a wildcard seriesList. For each series, determine the delta (last
value minus first value) and create a new series with the delta as its only (first) value.
Really only useful to create a bar graph showing metrics with the most change over a time period.
"""
results = []
for series in seriesList:
newValues = []
delta = seriesdelta(series)
newValues.append(delta)
newName = "mostChange(%s)" % series.name
newSeries = TimeSeries(newName, series.start, series.end, series.step,
newValues)
newSeries.pathExpression = newName
results.append(newSeries)
return results
def test_time_stack(self):
timespan = 3600 * 24 * 8 # 8 days
stop = int(time.time())
step = 100
series = TimeSeries('foo.bar',
stop - timespan,
stop,
step,
[x**1.5 for x in range(0, timespan, step)])
series[10] = None
series.pathExpression = 'foo.bar'
self.write_series(series, [(100, timespan)])
ctx = {'startTime': parseATTime('-1d'),
'endTime': parseATTime('now')}
stack = functions.timeStack(ctx, [series], '1d', 0, 7)
self.assertEqual(len(stack), 7)
stack = functions.timeStack(ctx, [series], '-1d', 0, 7)
self.assertEqual(len(stack), 7)
def test_holt_winters(self):
timespan = 3600 * 24 * 8 # 8 days
stop = int(time.time())
step = 100
series = TimeSeries('foo.bar', stop - timespan, stop, step,
[x**1.5 for x in range(0, timespan, step)])
series[10] = None
series.pathExpression = 'foo.bar'
self.write_series(series, [(100, timespan)])
ctx = {
'startTime': parseATTime('-1d'),
}
analysis = functions.holtWintersForecast(ctx, [series])
self.assertEqual(len(analysis), 1)
analysis = functions.holtWintersConfidenceBands(ctx, [series])
self.assertEqual(len(analysis), 2)
analysis = functions.holtWintersConfidenceArea(ctx, [series])
self.assertEqual(len(analysis), 2)
analysis = functions.holtWintersAberration(ctx, [series])
self.assertEqual(len(analysis), 1)
def ASAP(requestContext, seriesList, resolution=1000):
'''
use the ASAP smoothing on a series
https://arxiv.org/pdf/1703.00983.pdf
https://raw.githubusercontent.com/stanford-futuredata/ASAP/master/ASAP.py
:param requestContext:
:param seriesList:
:param resolution: either number of points to keep or a time resolution
:return: smoothed(seriesList)
'''
if not seriesList:
return []
windowInterval = None
if isinstance(resolution, six.string_types):
delta = parseTimeOffset(resolution)
windowInterval = to_seconds(delta)
if windowInterval:
previewSeconds = windowInterval
else:
previewSeconds = max([s.step for s in seriesList]) * int(resolution)
# ignore original data and pull new, including our preview
# data from earlier is needed to calculate the early results
newContext = requestContext.copy()
newContext['startTime'] = (requestContext['startTime'] -
timedelta(seconds=previewSeconds))
previewList = evaluateTokens(newContext, requestContext['args'][0])
result = []
for series in previewList:
if windowInterval:
# the resolution here is really the number of points to maintain
# so we need to convert the "seconds" to num points
windowPoints = round((series.end - series.start) / windowInterval)
else:
use_res = int(resolution)
if len(series) < use_res:
use_res = len(series)
windowPoints = use_res
if isinstance(resolution, six.string_types):
newName = 'asap(%s,"%s")' % (series.name, resolution)
else:
newName = "asap(%s,%s)" % (series.name, resolution)
step_guess = (series.end - series.start) // windowPoints
newSeries = TimeSeries(newName, series.start, series.end, step_guess,
[])
newSeries.pathExpression = newName
# detect "none" lists
if len([v for v in series if v is not None]) <= 1:
newSeries.extend(series)
else:
# the "resolution" is a suggestion,
# the algo will alter it some inorder
# to get the best view for things
new_s = smooth(series, windowPoints)
# steps need to be ints, so we must force the issue
new_step = round((series.end - series.start) / len(new_s))
newSeries.step = new_step
newSeries.extend(new_s)
result.append(newSeries)
return result
