diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py index 104718ed7db..23dd96e365a 100644 --- a/Lib/test/test_statistics.py +++ b/Lib/test/test_statistics.py @@ -18,6 +18,7 @@ from decimal import Decimal from fractions import Fraction +from test import support # Module to be tested. @@ -178,6 +179,23 @@ class _DoNothing: # We prefer this for testing numeric values that may not be exactly equal, # and avoid using TestCase.assertAlmostEqual, because it sucks :-) +py_statistics = support.import_fresh_module('statistics', blocked=['_statistics']) +c_statistics = support.import_fresh_module('statistics', fresh=['_statistics']) + + +class TestModules(unittest.TestCase): + func_names = ['_normal_dist_inv_cdf'] + + def test_py_functions(self): + for fname in self.func_names: + self.assertEqual(getattr(py_statistics, fname).__module__, 'statistics') + + @unittest.skipUnless(c_statistics, 'requires _statistics') + def test_c_functions(self): + for fname in self.func_names: + self.assertEqual(getattr(c_statistics, fname).__module__, '_statistics') + + class NumericTestCase(unittest.TestCase): """Unit test class for numeric work. @@ -2314,7 +2332,7 @@ def test_error_cases(self): quantiles([10, None, 30], n=4) # data is non-numeric -class TestNormalDist(unittest.TestCase): +class TestNormalDist: # General note on precision: The pdf(), cdf(), and overlap() methods # depend on functions in the math libraries that do not make @@ -2324,35 +2342,35 @@ class TestNormalDist(unittest.TestCase): # implementing our own implementations from scratch. def test_slots(self): - nd = statistics.NormalDist(300, 23) + nd = self.module.NormalDist(300, 23) with self.assertRaises(TypeError): vars(nd) self.assertEqual(tuple(nd.__slots__), ('_mu', '_sigma')) def test_instantiation_and_attributes(self): - nd = statistics.NormalDist(500, 17) + nd = self.module.NormalDist(500, 17) self.assertEqual(nd.mean, 500) self.assertEqual(nd.stdev, 17) self.assertEqual(nd.variance, 17**2) # default arguments - nd = statistics.NormalDist() + nd = self.module.NormalDist() self.assertEqual(nd.mean, 0) self.assertEqual(nd.stdev, 1) self.assertEqual(nd.variance, 1**2) # error case: negative sigma - with self.assertRaises(statistics.StatisticsError): - statistics.NormalDist(500, -10) + with self.assertRaises(self.module.StatisticsError): + self.module.NormalDist(500, -10) # verify that subclass type is honored - class NewNormalDist(statistics.NormalDist): + class NewNormalDist(self.module.NormalDist): pass nnd = NewNormalDist(200, 5) self.assertEqual(type(nnd), NewNormalDist) def test_alternative_constructor(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist data = [96, 107, 90, 92, 110] # list input self.assertEqual(NormalDist.from_samples(data), NormalDist(99, 9)) @@ -2361,9 +2379,9 @@ def test_alternative_constructor(self): # iterator input self.assertEqual(NormalDist.from_samples(iter(data)), NormalDist(99, 9)) # error cases - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): NormalDist.from_samples([]) # empty input - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): NormalDist.from_samples([10]) # only one input # verify that subclass type is honored @@ -2373,7 +2391,7 @@ class NewNormalDist(NormalDist): self.assertEqual(type(nnd), NewNormalDist) def test_sample_generation(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist mu, sigma = 10_000, 3.0 X = NormalDist(mu, sigma) n = 1_000 @@ -2381,7 +2399,7 @@ def test_sample_generation(self): self.assertEqual(len(data), n) self.assertEqual(set(map(type, data)), {float}) # mean(data) expected to fall within 8 standard deviations - xbar = statistics.mean(data) + xbar = self.module.mean(data) self.assertTrue(mu - sigma*8 <= xbar <= mu + sigma*8) # verify that seeding makes reproducible sequences @@ -2395,7 +2413,7 @@ def test_sample_generation(self): self.assertNotEqual(data1, data2) def test_pdf(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist X = NormalDist(100, 15) # Verify peak around center self.assertLess(X.pdf(99), X.pdf(100)) @@ -2426,7 +2444,7 @@ def test_pdf(self): self.assertAlmostEqual(Z.pdf(-x / 100.0), px, places=4) # Error case: variance is zero Y = NormalDist(100, 0) - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): Y.pdf(90) # Special values self.assertEqual(X.pdf(float('-Inf')), 0.0) @@ -2434,7 +2452,7 @@ def test_pdf(self): self.assertTrue(math.isnan(X.pdf(float('NaN')))) def test_cdf(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist X = NormalDist(100, 15) cdfs = [X.cdf(x) for x in range(1, 200)] self.assertEqual(set(map(type, cdfs)), {float}) @@ -2456,7 +2474,7 @@ def test_cdf(self): self.assertAlmostEqual(Z.cdf(-z), 1.0 - cum_prob, places=5) # Error case: variance is zero Y = NormalDist(100, 0) - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): Y.cdf(90) # Special values self.assertEqual(X.cdf(float('-Inf')), 0.0) @@ -2465,7 +2483,7 @@ def test_cdf(self): @support.skip_if_pgo_task def test_inv_cdf(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist # Center case should be exact. iq = NormalDist(100, 15) @@ -2513,15 +2531,15 @@ def test_inv_cdf(self): self.assertAlmostEqual(iq.inv_cdf(iq.cdf(x)), x, places=5) # Error cases: - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): iq.inv_cdf(0.0) # p is zero - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): iq.inv_cdf(-0.1) # p under zero - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): iq.inv_cdf(1.0) # p is one - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): iq.inv_cdf(1.1) # p over one - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): iq = NormalDist(100, 0) # sigma is zero iq.inv_cdf(0.5) @@ -2529,7 +2547,7 @@ def test_inv_cdf(self): self.assertTrue(math.isnan(Z.inv_cdf(float('NaN')))) def test_overlap(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist # Match examples from Imman and Bradley for X1, X2, published_result in [ @@ -2586,26 +2604,26 @@ def overlap_numeric(X, Y, *, steps=8_192, z=5): X.overlap(X, X) # too may arguments with self.assertRaises(TypeError): X.overlap(None) # right operand not a NormalDist - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): X.overlap(NormalDist(1, 0)) # right operand sigma is zero - with self.assertRaises(statistics.StatisticsError): + with self.assertRaises(self.module.StatisticsError): NormalDist(1, 0).overlap(X) # left operand sigma is zero def test_properties(self): - X = statistics.NormalDist(100, 15) + X = self.module.NormalDist(100, 15) self.assertEqual(X.mean, 100) self.assertEqual(X.stdev, 15) self.assertEqual(X.variance, 225) def test_same_type_addition_and_subtraction(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist X = NormalDist(100, 12) Y = NormalDist(40, 5) self.assertEqual(X + Y, NormalDist(140, 13)) # __add__ self.assertEqual(X - Y, NormalDist(60, 13)) # __sub__ def test_translation_and_scaling(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist X = NormalDist(100, 15) y = 10 self.assertEqual(+X, NormalDist(100, 15)) # __pos__ @@ -2621,7 +2639,7 @@ def test_translation_and_scaling(self): y / X def test_unary_operations(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist X = NormalDist(100, 12) Y = +X self.assertIsNot(X, Y) @@ -2633,7 +2651,7 @@ def test_unary_operations(self): self.assertEqual(X.stdev, Y.stdev) def test_equality(self): - NormalDist = statistics.NormalDist + NormalDist = self.module.NormalDist nd1 = NormalDist() nd2 = NormalDist(2, 4) nd3 = NormalDist() @@ -2673,7 +2691,7 @@ def __init__(self, mu, sigma): self.assertNotEqual(nd, lnd) def test_pickle_and_copy(self): - nd = statistics.NormalDist(37.5, 5.625) + nd = self.module.NormalDist(37.5, 5.625) nd1 = copy.copy(nd) self.assertEqual(nd, nd1) nd2 = copy.deepcopy(nd) @@ -2682,14 +2700,36 @@ def test_pickle_and_copy(self): self.assertEqual(nd, nd3) def test_hashability(self): - ND = statistics.NormalDist + ND = self.module.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) + nd = self.module.NormalDist(37.5, 5.625) self.assertEqual(repr(nd), 'NormalDist(mu=37.5, sigma=5.625)') +# Swapping the sys.modules['statistics'] is to solving the +# _pickle.PicklingError: +# Can't pickle : +# it's not the same object as statistics.NormalDist +class TestNormalDistPython(unittest.TestCase, TestNormalDist): + module = py_statistics + def setUp(self): + sys.modules['statistics'] = self.module + + def tearDown(self): + sys.modules['statistics'] = statistics + + +@unittest.skipUnless(c_statistics, 'requires _statistics') +class TestNormalDistC(unittest.TestCase, TestNormalDist): + module = c_statistics + def setUp(self): + sys.modules['statistics'] = self.module + + def tearDown(self): + sys.modules['statistics'] = statistics + # === Run tests === diff --git a/Misc/NEWS.d/next/Library/2019-08-24-16-54-49.bpo-37798.7mRQCk.rst b/Misc/NEWS.d/next/Library/2019-08-24-16-54-49.bpo-37798.7mRQCk.rst new file mode 100644 index 00000000000..25cfa004788 --- /dev/null +++ b/Misc/NEWS.d/next/Library/2019-08-24-16-54-49.bpo-37798.7mRQCk.rst @@ -0,0 +1,2 @@ +Update test_statistics.py to verify that the statistics module works well +for both C and Python implementations. Patch by Dong-hee Na