def setUp(self):
self.merge_triples = ((
distribution.create_exponential(3, 2, 0.1),
distribution.create_exponential(3, 2, 0.1),
distribution.create_exponential(4, 2, 0.1),
), (distribution.create_linear(3, 0.2, 0.1),
distribution.create_linear(3, 0.2, 0.1),
distribution.create_linear(4, 0.2, 0.1)), (
distribution.create_explicit([0.1, 0.3]),
distribution.create_explicit([0.1, 0.3]),
distribution.create_explicit([0.1, 0.3, 0.5]),
))
for d1, d2, _ in self.merge_triples:
distribution.add_sample(_LOW_SAMPLE, d1)
distribution.add_sample(_HIGH_SAMPLE, d2)
def test_should_succeed_for_delta_metrics_with_the_distribution_type(self):
test_distribution = distribution.create_explicit([0.1, 0.3, 0.5])
distribution.add_sample(0.4, test_distribution)
v = metric_value.create(labels=self.TEST_LABELS,
distributionValue=test_distribution)
want = 2 * test_distribution.count
got = metric_value.merge(MetricKind.DELTA, v, v)
expect(got.distributionValue.count).to(equal(want))
def test_should_succeed_for_delta_metrics_with_the_distribution_type(self):
test_distribution = distribution.create_explicit([0.1, 0.3, 0.5])
distribution.add_sample(0.4, test_distribution)
v = metric_value.create(labels=self.TEST_LABELS,
distributionValue=test_distribution)
want = 2 * test_distribution.count
got = metric_value.merge(MetricKind.DELTA, v, v)
expect(got.distributionValue.count).to(equal(want))
def setUp(self):
self.merge_triples = (
(
distribution.create_exponential(3, 2, 0.1),
distribution.create_exponential(3, 2, 0.1),
distribution.create_exponential(4, 2, 0.1),
),(
distribution.create_linear(3, 0.2, 0.1),
distribution.create_linear(3, 0.2, 0.1),
distribution.create_linear(4, 0.2, 0.1)
),(
distribution.create_explicit([0.1, 0.3]),
distribution.create_explicit([0.1, 0.3]),
distribution.create_explicit([0.1, 0.3, 0.5]),
)
)
for d1, d2, _ in self.merge_triples:
distribution.add_sample(_LOW_SAMPLE, d1)
distribution.add_sample(_HIGH_SAMPLE, d2)
def _make_explicit_dist():
return distribution.create_explicit([0.1, 0.3, 0.5, 0.7])
def test_should_succeed_if_input_bounds_are_unsorted(self):
want = [0.1, 0.2, 0.3]
got = distribution.create_explicit([0.3, 0.1, 0.2])
expect(got.explicitBuckets.bounds).to(equal(want))
def test_should_succeed_if_inputs_are_ok(self):
want = [0.1, 0.2, 0.3]
got = distribution.create_explicit([0.1, 0.2, 0.3])
expect(got.explicitBuckets.bounds).to(equal(want))
expect(len(got.bucketCounts)).to(equal(len(want) + 1))
def test_should_fail_if_there_are_matching_bounds(self):
testf = lambda: distribution.create_explicit([0.0, 0.1, 0.1])
expect(testf).to(raise_error(ValueError))
def _make_explicit_dist():
return distribution.create_explicit([0.1, 0.3, 0.5, 0.7])
def test_should_succeed_if_input_bounds_are_unsorted(self):
want = [0.1, 0.2, 0.3]
got = distribution.create_explicit([0.3, 0.1, 0.2])
expect(got.explicitBuckets.bounds).to(equal(want))
def test_should_succeed_if_inputs_are_ok(self):
want = [0.1, 0.2, 0.3]
got = distribution.create_explicit([0.1, 0.2, 0.3])
expect(got.explicitBuckets.bounds).to(equal(want))
expect(len(got.bucketCounts)).to(equal(len(want) + 1))
def test_should_fail_if_there_are_matching_bounds(self):
testf = lambda: distribution.create_explicit([0.0, 0.1, 0.1])
expect(testf).to(raise_error(ValueError))