def test_t_one_observation(self):
"""t_one_observation should match p. 228 of Sokal and Rohlf"""
sample = np.array([4.02, 3.88, 3.34, 3.87, 3.18])
x = 3.02
# note that this differs after the 3rd decimal place from what's in
# the book, because Sokal and Rohlf round their intermediate steps...
np.testing.assert_allclose(t_one_observation(x, sample),
(-1.5637254, 0.1929248))
def test_t_one_observation(self):
"""t_one_observation should match p. 228 of Sokal and Rohlf"""
sample = np.array([4.02, 3.88, 3.34, 3.87, 3.18])
x = 3.02
# note that this differs after the 3rd decimal place from what's in
# the book, because Sokal and Rohlf round their intermediate steps...
np.testing.assert_allclose(t_one_observation(x, sample),
(-1.5637254, 0.1929248))
def test_t_one_observation_no_variance(self):
"""t_one_observation should correctly handle an invariant list."""
sample = np.array([1.0, 1.0, 1.0])
# Can't perform test if invariant list's single value matches x,
# regardless of none_on_zero_variance.
self.assertEqual(t_one_observation(1, sample), (None, None))
self.assertEqual(t_one_observation(1, sample,
none_on_zero_variance=False),
(None, None))
# Test correct handling of none_on_zero_variance.
self.assertEqual(t_one_observation(2, sample), (None, None))
self.assertEqual(t_one_observation(2, sample,
none_on_zero_variance=False),
(float('inf'), 0.0))
self.assertEqual(t_one_observation(2, sample,
none_on_zero_variance=False,
tails='low'),
(float('inf'), 1.0))
def test_t_one_observation_no_variance(self):
"""t_one_observation should correctly handle an invariant list."""
sample = np.array([1.0, 1.0, 1.0])
# Can't perform test if invariant list's single value matches x,
# regardless of none_on_zero_variance.
self.assertEqual(t_one_observation(1, sample), (None, None))
self.assertEqual(t_one_observation(1, sample,
none_on_zero_variance=False),
(None, None))
# Test correct handling of none_on_zero_variance.
self.assertEqual(t_one_observation(2, sample), (None, None))
self.assertEqual(t_one_observation(2, sample,
none_on_zero_variance=False),
(float('inf'), 0.0))
self.assertEqual(t_one_observation(2, sample,
none_on_zero_variance=False,
tails='low'),
(float('inf'), 1.0))
