def test_normal(mean, stddev, offset):
# create 100 buckets, from -5.0 to 4.9 inclusive
x_values = [(.01 * i - 5) for i in range(1001)]
y_values = [(norm.cdf(right) - norm.cdf(left))
for left, right in zip(x_values, x_values[1:])]
points = [Point(x, y) for x, y in zip(x_values, y_values)]
assert "???" == analysers.get_analysis(points=points)
def test_normal(mean, stddev, offset):
# create 100 buckets, from -5.0 to 4.9 inclusive
x_values = [(.01 * i - 5) for i in range(1001)]
y_values = [100 * (phi(right) - phi(left))
for left, right in zip(x_values, x_values[1:])]
points = [Point(x, y) for x, y in zip(x_values, y_values)]
analyser = analysers.get_analysis(points=points)
assert analyser['name'] == 'normal'
def test_perfect_linear(gradient, constant):
"""
First, check we get a perfect validity score for
straight lines.
Then ensure that the analyser selector chooses
the LinearAnalyser for these series.
"""
points = [Point(i, gradient * i + constant) for i in range(20)]
ld = analysers.LinearDistribution(points=points)
assert ld.get_validity() == 1.0
result = analysers.get_analysis(points=points)
assert result["name"] == "linear"
assert result["p_value"] == 1.0
