def test_extreme_percentiles_return_min_and_max(self, empty_tdigest): t = TDigest() data = random.randn(10000) t.batch_update(data) assert t.percentile(100.0) == data.max() assert t.percentile(0) == data.min() assert t.percentile(0.1) > data.min() assert t.percentile(0.999) < data.max()
def test_ints(self): t = TDigest() t.batch_update([1, 2, 3]) assert abs(t.percentile(50) - 2) < 0.0001 t = TDigest() x = [1, 2, 2, 2, 2, 2, 2, 2, 3] t.batch_update(x) assert t.percentile(50) == 2 assert sum([c.count for c in t.C.values()]) == len(x)
def test_uniform(self): t = TDigest() x = random.random(size=10000) t.batch_update(x) assert abs(t.percentile(50) - 0.5) < 0.01 assert abs(t.percentile(10) - 0.1) < 0.01 assert abs(t.percentile(90) - 0.9) < 0.01 assert abs(t.percentile(1) - 0.01) < 0.005 assert abs(t.percentile(99) - 0.99) < 0.005 assert abs(t.percentile(0.1) - 0.001) < 0.001 assert abs(t.percentile(99.9) - 0.999) < 0.001
def test_percentile_at_border_returns_an_intermediate_value(self, empty_tdigest): data = [62.0, 202.0, 1415.0, 1433.0] t = TDigest() t.batch_update(data) assert t.percentile(25) == 132.0
def test_negative_extreme_percentile_is_still_positive(self, empty_tdigest): # Test https://github.com/CamDavidsonPilon/tdigest/issues/16 t = TDigest() t.batch_update([62.0, 202.0, 1415.0, 1433.0]) print(t.percentile(26)) assert t.percentile(26) > 0