def test_collate_effect_size_2_power(self):
# The known effect is used as a reference elsewhere
test_means, test_bounds = collate_effect_size(counts=self.counts,
powers=self.power2,
alpha=self.alpha)
npt.assert_almost_equal(self.effects, test_means, 4)
npt.assert_almost_equal(self.bounds, test_bounds, 4)
def test_collate_effect_size_2_power(self):
# The known effect is used as a reference elsewhere
test_means, test_bounds = collate_effect_size(counts=self.counts,
powers=self.power2,
alpha=self.alpha)
npt.assert_almost_equal(self.effects, test_means, 4)
npt.assert_almost_equal(self.bounds, test_bounds, 4)
def test_collate_effect_size_1_power(self):
known_means = np.array([0.72374193, 0.5795861])
known_bounds = np.array([0.17654622, 0.06264818])
test_means, test_bounds = collate_effect_size(counts=self.counts,
powers=self.power1,
alpha=self.alpha)
npt.assert_almost_equal(known_means, test_means, 4)
npt.assert_almost_equal(known_bounds, test_bounds, 4)
def test_collate_effect_size_1_power(self):
known_means = np.array([0.72374193, 0.5795861])
known_bounds = np.array([0.17654622, 0.06264818])
test_means, test_bounds = collate_effect_size(counts=self.counts,
powers=self.power1,
alpha=self.alpha)
npt.assert_almost_equal(known_means, test_means, 4)
npt.assert_almost_equal(known_bounds, test_bounds, 4)
def test_collate_effect_size_nan(self):
power = [count * 0 for count in self.counts]
test_mean, test_bounds = collate_effect_size(counts=self.counts,
powers=power,
alpha=self.alpha)
self.assertTrue(np.isnan(test_mean[0]))
self.assertTrue(np.isnan(test_bounds[0]))
def test_collate_effect_size_nan(self):
power = [count*0 for count in self.counts]
test_mean, test_bounds = collate_effect_size(counts=self.counts,
powers=power,
alpha=self.alpha)
self.assertTrue(np.isnan(test_mean[0]))
self.assertTrue(np.isnan(test_bounds[0]))
def test_collate_effect_size_count_for_power_2d(self):
with self.assertRaises(ValueError):
collate_effect_size(counts=[self.counts[0]],
powers=self.power2[1],
alpha=0.05)
def test_collate_effect_size_count_shape_error(self):
with self.assertRaises(TypeError):
collate_effect_size(counts=self.power2,
powers=self.power2,
alpha=self.alpha)
def test_collate_effect_size_unequal_counts_error(self):
with self.assertRaises(ValueError):
collate_effect_size(counts=[np.array([1, 2, 3])],
powers=self.power2,
alpha=self.alpha)
def test_collate_effect_size_count_for_power_2d(self):
with self.assertRaises(ValueError):
collate_effect_size(counts=[self.counts[0]],
powers=self.power2[1],
alpha=0.05)
def test_collate_effect_size_count_shape_error(self):
with self.assertRaises(TypeError):
collate_effect_size(counts=self.power2,
powers=self.power2,
alpha=self.alpha)
def test_collate_effect_size_unequal_counts_error(self):
with self.assertRaises(ValueError):
collate_effect_size(counts=[np.array([1, 2, 3])],
powers=self.power2,
alpha=self.alpha)
