def test_mmax_above_global_maximum_raises_error(self):
model = mass.SinglePeakSmoothedMassDistribution(mmin=5, mmax=150)
parameters = self.gauss_prior.sample()
parameters.update(self.power_prior.sample())
parameters.update(self.smooth_prior.sample())
parameters["mmax"] = 200
with self.assertRaises(ValueError):
model(self.dataset, **parameters)
def test_single_peak_normalised(self):
norms = list()
for ii in range(self.n_test):
parameters = self.power_prior.sample()
parameters.update(self.gauss_prior.sample())
parameters.update(self.smooth_prior.sample())
p_m = mass.SinglePeakSmoothedMassDistribution()(self.dataset, **parameters)
norms.append(trapz(trapz(p_m, self.m1s), self.qs))
self.assertAlmostEqual(_max_abs_difference(norms, 1.0), 0.0, 2)
def test_single_peak_delta_m_zero_matches_two_component_primary_mass_ratio(self):
max_diffs = list()
for ii in range(self.n_test):
parameters = self.power_prior.sample()
parameters.update(self.gauss_prior.sample())
p_m1 = mass.two_component_primary_mass_ratio(self.dataset, **parameters)
parameters["delta_m"] = 0
p_m2 = mass.SinglePeakSmoothedMassDistribution()(self.dataset, **parameters)
max_diffs.append(_max_abs_difference(p_m1, p_m2))
self.assertAlmostEqual(max(max_diffs), 0.0)
def test_set_minimum_and_maximum(self):
model = mass.SinglePeakSmoothedMassDistribution(mmin=5, mmax=150)
parameters = self.gauss_prior.sample()
parameters.update(self.power_prior.sample())
parameters.update(self.smooth_prior.sample())
parameters["mpp"] = 130
parameters["sigpp"] = 1
parameters["lam"] = 0.5
parameters["mmin"] = 5
self.assertEqual(
model(dict(mass_1=8 * np.ones(5), mass_ratio=0.5 * np.ones(5)),
**parameters)[0],
0,
)
self.assertGreater(
model(dict(mass_1=130 * np.ones(5), mass_ratio=0.9 * np.ones(5)),
**parameters)[0],
0,
)