def test__calculate_power_curve_ratio_error(self): with self.assertRaises(ValueError): _calculate_power_curve(self.f, self.pop, self.num_samps, ratio=np.array([0.1, 0.2, 0.3]), num_iter=100)
def test__calculate_power_curve_default(self): # Sets the known output known = np.array([0.509, 0.822, 0.962, 0.997, 1.000, 1.000, 1.000, 1.000, 1.000]) # Generates the test values test = _calculate_power_curve(self.f, self.pop, self.num_samps, num_iter=100) # Checks the samples returned sanely npt.assert_allclose(test, known, rtol=0.1, atol=0.01)
def test__calculate_power_curve_ratio(self): # Sets the know output known = np.array([0.096, 0.333, 0.493, 0.743, 0.824, 0.937, 0.969, 0.996, 0.998]) # Generates the test values test = _calculate_power_curve(self.f, self.pop, self.num_samps, ratio=np.array([0.25, 0.75]), num_iter=100) # Checks the samples returned sanely npt.assert_allclose(test, known, rtol=0.1, atol=0.1)
def test__calculate_power_curve_alpha(self): # Sets the know output known = np.array([0.31, 0.568, 0.842, 0.954, 0.995, 1.000, 1.000, 1.000, 1.000]) # Generates the test values test = _calculate_power_curve(self.f, self.pop, self.num_samps, alpha=0.01, num_iter=100) # Checks the samples returned sanely npt.assert_allclose(test, known, rtol=0.1, atol=0.1)