def to_point(self, timestamp):
"""Get a Point conversion of this aggregation.
This method creates a :class: `opencensus.metrics.export.point.Point`
with a :class: `opencensus.metrics.export.value.ValueDistribution`
value, and creates buckets and exemplars for that distribution from the
appropriate classes in the `metrics` package.
:type timestamp: :class: `datetime.datetime`
:param timestamp: The time to report the point as having been recorded.
:rtype: :class: `opencensus.metrics.export.point.Point`
:return: a :class: `opencensus.metrics.export.value.ValueDistribution`
-valued Point.
"""
buckets = [None] * len(self.counts_per_bucket)
for ii, count in enumerate(self.counts_per_bucket):
stat_ex = self.exemplars.get(ii, None)
if stat_ex is not None:
metric_ex = value.Exemplar(stat_ex.value, stat_ex.timestamp,
copy.copy(stat_ex.attachments))
buckets[ii] = value.Bucket(count, metric_ex)
else:
buckets[ii] = value.Bucket(count)
bucket_options = value.BucketOptions(value.Explicit(self.bounds))
return point.Point(
value.ValueDistribution(
count=self.count_data,
sum_=self.sum,
sum_of_squared_deviation=self.sum_of_sqd_deviations,
bucket_options=bucket_options,
buckets=buckets), timestamp)
def test_init_no_histogram(self):
distribution = value_module.ValueDistribution(
VD_COUNT, VD_SUM, VD_SUM_OF_SQUARED_DEVIATION, [], None)
self.assertEqual(distribution.count, VD_COUNT)
self.assertEqual(distribution.sum, VD_SUM)
self.assertEqual(distribution.sum_of_squared_deviation,
VD_SUM_OF_SQUARED_DEVIATION)
self.assertEqual(distribution.bucket_bounds, [])
self.assertEqual(distribution.buckets, None)
def test_init_no_histogram(self):
distribution = value_module.ValueDistribution(
VD_COUNT, VD_SUM, VD_SUM_OF_SQUARED_DEVIATION,
value_module.BucketOptions(), None)
self.assertEqual(distribution.count, VD_COUNT)
self.assertEqual(distribution.sum, VD_SUM)
self.assertEqual(distribution.sum_of_squared_deviation,
VD_SUM_OF_SQUARED_DEVIATION)
self.assertIsNone(distribution.bucket_options.type_)
self.assertEqual(distribution.buckets, None)
def test_init(self):
distribution = value_module.ValueDistribution(
VD_COUNT, VD_SUM, VD_SUM_OF_SQUARED_DEVIATION, BUCKET_BOUNDS,
BUCKETS)
self.assertEqual(distribution.count, VD_COUNT)
self.assertEqual(distribution.sum, VD_SUM)
self.assertEqual(distribution.sum_of_squared_deviation,
VD_SUM_OF_SQUARED_DEVIATION)
self.assertEqual(distribution.bucket_bounds, BUCKET_BOUNDS)
self.assertEqual(distribution.buckets, BUCKETS)
def _create_distribution_value(count=1,
sum_=0,
sum_of_squared_deviation=0,
bounds=[],
buckets=[]):
return value.ValueDistribution(
count=count,
sum_=sum_,
sum_of_squared_deviation=sum_of_squared_deviation,
bucket_options=value.BucketOptions(type_=value.Explicit(
bounds=bounds)),
buckets=buckets)
def test_init_bad_args(self):
value_module.ValueDistribution(VD_COUNT, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_OPTIONS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(-1, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_OPTIONS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(0, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_OPTIONS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(0, 0, VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_OPTIONS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(
VD_COUNT, VD_SUM, VD_SUM_OF_SQUARED_DEVIATION, None, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(0, 0, 0, BUCKET_OPTIONS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(VD_COUNT - 1, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_OPTIONS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(VD_COUNT, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION,
value_module.BucketOptions(),
BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(VD_COUNT, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_OPTIONS, BUCKETS[:-1])
def setUp(self):
self.double_value = value_module.Value.double_value(55.5)
self.long_value = value_module.Value.long_value(9876543210)
self.timestamp = '2018-10-06T17:57:57.936475Z'
value_at_percentile = [summary_module.ValueAtPercentile(99.5, 10.2)]
snapshot = summary_module.Snapshot(10, 87.07, value_at_percentile)
self.summary = summary_module.Summary(10, 6.6, snapshot)
self.summary_value = value_module.Value.summary_value(self.summary)
self.distribution_value = value_module.ValueDistribution(
100,
1000.0,
10.0,
list(range(11)),
[value_module.Bucket(10, None) for ii in range(10)],
)
def test_init_bad_args(self):
with self.assertRaises(ValueError):
value_module.ValueDistribution(-1, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_BOUNDS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(0, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_BOUNDS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(0, 0, VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_BOUNDS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(VD_COUNT, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION, None,
BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(VD_COUNT, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION, [],
BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(0, 0, 0, BUCKET_BOUNDS, BUCKETS)
with self.assertRaises(ValueError):
value_module.ValueDistribution(
VD_COUNT, VD_SUM, VD_SUM_OF_SQUARED_DEVIATION, [1, 1],
[value_module.Bucket(1, None),
value_module.Bucket(1, None)])
with self.assertRaises(ValueError):
value_module.ValueDistribution(VD_COUNT - 1, VD_SUM,
VD_SUM_OF_SQUARED_DEVIATION,
BUCKET_BOUNDS, BUCKETS)