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))