def test_quantile_out_of_bouns():
h = distogram.Distogram()
for i in [1, 2, 3, 4, 5, 6, 6.7, 6.1]:
h = distogram.update(h, i)
assert distogram.quantile(h, -0.2) is None
assert distogram.quantile(h, 10) is None
def test_quantile_on_right():
h = distogram.Distogram(bin_count=6)
data = [12.3, 8.2, 100.53, 23.5, 13.98, 200, 200.2, 200.8, 200.4, 200.1]
for i in data:
h = distogram.update(h, i)
assert distogram.quantile(h, 0.99) == approx(np.quantile(data, 0.99),
rel=0.01)
assert distogram.quantile(h, 0.85) == approx(np.quantile(data, 0.85),
rel=0.01)
def test_normal():
# normal = np.random.normal(0,1, 1000)
normal = [random.normalvariate(0.0, 1.0) for _ in range(10000)]
h = distogram.Distogram(bin_count=64)
for i in normal:
h = distogram.update(h, i)
assert distogram.quantile(h, 0.5) == approx(np.quantile(normal, 0.5),
abs=0.2)
assert distogram.quantile(h, 0.95) == approx(np.quantile(normal, 0.95),
abs=0.2)
def test_quantile_on_left():
h = distogram.Distogram(bin_count=6)
data = [12.3, 5.2, 5.4, 4.9, 5.5, 5.6, 8.2, 30.53, 23.5, 13.98]
for i in data:
h = distogram.update(h, i)
assert distogram.quantile(h, 0.01) == approx(np.quantile(data, 0.01),
rel=0.01)
assert distogram.quantile(h, 0.05) == approx(np.quantile(data, 0.05),
rel=0.05)
assert distogram.quantile(h, 0.25) == approx(np.quantile(data, 0.25),
rel=0.05)
def test_quantile():
h = distogram.Distogram(bin_count=3)
h = distogram.update(h, 16, count=4)
h = distogram.update(h, 23, count=3)
h = distogram.update(h, 28, count=5)
assert distogram.quantile(h, 0.5) == approx(23.625)
def test_quantile_not_enough_elemnts():
h = distogram.Distogram(bin_count=10)
for i in [12.3, 5.4, 8.2, 100.53, 23.5, 13.98]:
h = distogram.update(h, i)
assert distogram.quantile(h, 0.5) == approx(13.14)
def quantile(value):
'''Returns a quantile value of the distribution.
The source can be an Observable or a MuxObservable.
Args:
value: The quantile value to compute, between 0 and 1.
Returns:
An observable emitting the quantile value of each source items.
'''
return rx.pipe(rs.ops.map(lambda i: distogram.quantile(i, value)))
def test_quantile_empty():
h = distogram.Distogram()
assert distogram.quantile(h, 0.3) is None