bpo-36324: Make internal attributes for statistics.NormalDist() private. (GH-14871)
* Make internals private
* Finish making mu and sigma private
* Add missing __hash__() method
* Add blurb
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
index 946c742..ed2f657 100644
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -2326,18 +2326,18 @@
nd = statistics.NormalDist(300, 23)
with self.assertRaises(TypeError):
vars(nd)
- self.assertEqual(tuple(nd.__slots__), ('mu', 'sigma'))
+ self.assertEqual(tuple(nd.__slots__), ('_mu', '_sigma'))
def test_instantiation_and_attributes(self):
nd = statistics.NormalDist(500, 17)
- self.assertEqual(nd.mu, 500)
- self.assertEqual(nd.sigma, 17)
+ self.assertEqual(nd.mean, 500)
+ self.assertEqual(nd.stdev, 17)
self.assertEqual(nd.variance, 17**2)
# default arguments
nd = statistics.NormalDist()
- self.assertEqual(nd.mu, 0)
- self.assertEqual(nd.sigma, 1)
+ self.assertEqual(nd.mean, 0)
+ self.assertEqual(nd.stdev, 1)
self.assertEqual(nd.variance, 1**2)
# error case: negative sigma
@@ -2520,10 +2520,7 @@
with self.assertRaises(statistics.StatisticsError):
iq.inv_cdf(1.1) # p over one
with self.assertRaises(statistics.StatisticsError):
- iq.sigma = 0.0 # sigma is zero
- iq.inv_cdf(0.5)
- with self.assertRaises(statistics.StatisticsError):
- iq.sigma = -0.1 # sigma under zero
+ iq = NormalDist(100, 0) # sigma is zero
iq.inv_cdf(0.5)
# Special values
@@ -2544,8 +2541,8 @@
def overlap_numeric(X, Y, *, steps=8_192, z=5):
'Numerical integration cross-check for overlap() '
fsum = math.fsum
- center = (X.mu + Y.mu) / 2.0
- width = z * max(X.sigma, Y.sigma)
+ center = (X.mean + Y.mean) / 2.0
+ width = z * max(X.stdev, Y.stdev)
start = center - width
dx = 2.0 * width / steps
x_arr = [start + i*dx for i in range(steps)]
@@ -2626,12 +2623,12 @@
X = NormalDist(100, 12)
Y = +X
self.assertIsNot(X, Y)
- self.assertEqual(X.mu, Y.mu)
- self.assertEqual(X.sigma, Y.sigma)
+ self.assertEqual(X.mean, Y.mean)
+ self.assertEqual(X.stdev, Y.stdev)
Y = -X
self.assertIsNot(X, Y)
- self.assertEqual(X.mu, -Y.mu)
- self.assertEqual(X.sigma, Y.sigma)
+ self.assertEqual(X.mean, -Y.mean)
+ self.assertEqual(X.stdev, Y.stdev)
def test_equality(self):
NormalDist = statistics.NormalDist
@@ -2682,6 +2679,11 @@
nd3 = pickle.loads(pickle.dumps(nd))
self.assertEqual(nd, nd3)
+ def test_hashability(self):
+ ND = statistics.NormalDist
+ s = {ND(100, 15), ND(100.0, 15.0), ND(100, 10), ND(95, 15), ND(100, 15)}
+ self.assertEqual(len(s), 3)
+
def test_repr(self):
nd = statistics.NormalDist(37.5, 5.625)
self.assertEqual(repr(nd), 'NormalDist(mu=37.5, sigma=5.625)')