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)
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)