def test_power_law_primary_secondary_zero_below_mmin(self):
for ii in range(self.n_test):
parameters = self.power_prior.sample()
p_m = mass.power_law_primary_secondary_independent(
self.dataset, **parameters)
self.assertEqual(
xp.max(p_m[self.dataset["mass_2"] <= parameters["mmin"]]), 0.0)
def test_spin_magnitude_normalised(self):
norms = list()
for ii in range(self.n_test):
parameters = self.prior.sample()
temp = spin.iid_spin_magnitude_beta(self.test_data, **parameters)
norms.append(trapz(trapz(temp, self.a_array), self.a_array))
self.assertAlmostEqual(float(xp.max(xp.abs(1 - xp.asarray(norms)))), 0, 1)
def test_power_law_primary_mass_ratio_zero_above_mmax(self):
for ii in range(self.n_test):
parameters = self.power_prior.sample()
p_m = mass.double_power_law_primary_power_law_mass_ratio(
self.dataset, **parameters)
self.assertEqual(
xp.max(p_m[self.dataset["mass_1"] >= parameters["mmax"]]), 0.0)
def test_fhf_normalised(self):
norms = list()
for _ in range(self.n_test):
lamb = np.random.uniform(-15, 15)
p_z = redshift.power_law_redshift(self.test_data, lamb)
norms.append(trapz(p_z, self.zs))
self.assertAlmostEqual(xp.max(xp.abs(xp.asarray(norms) - 1)), 0.0)
def test_two_component_primary_mass_ratio_zero_below_mmin(self):
m2s = self.dataset["mass_1"] * self.dataset["mass_ratio"]
for ii in range(self.n_test):
parameters = self.power_prior.sample()
parameters.update(self.gauss_prior.sample())
p_m = mass.two_component_primary_mass_ratio(self.dataset, **parameters)
self.assertEqual(xp.max(p_m[m2s <= parameters["mmin"]]), 0.0)
def test_power_law_primary_secondary_zero_above_mmax(self):
for ii in range(self.n_test):
parameters = self.power_prior.sample()
del parameters["beta"]
p_m = mass.power_law_primary_secondary_identical(
self.dataset, **parameters)
self.assertEqual(
xp.max(p_m[self.dataset["mass_1"] >= parameters["mmax"]]), 0.0)
def test_iid_matches_independent_tilts(self):
iid_params = dict(xi_spin=0.5, sigma_spin=0.5)
ind_params = dict(xi_spin=0.5, sigma_1=0.5, sigma_2=0.5)
self.assertEquals(0.0, xp.max(
spin.iid_spin_orientation_gaussian_isotropic(
self.test_data, **iid_params) -
spin.independent_spin_orientation_gaussian_isotropic(
self.test_data, **ind_params)))
def test_spin_orientation_normalised(self):
norms = list()
for ii in range(self.n_test):
parameters = self.prior.sample()
temp = spin.iid_spin_orientation_gaussian_isotropic(
self.test_data, **parameters)
norms.append(trapz(trapz(temp, self.costilts), self.costilts))
self.assertAlmostEqual(float(xp.max(xp.abs(1 - xp.asarray(norms)))), 0, 5)
def test_two_component_primary_secondary_zero_below_mmin(self):
for ii in range(self.n_test):
parameters = self.power_prior.sample()
parameters.update(self.gauss_prior.sample())
del parameters["beta"]
p_m = mass.two_component_primary_secondary_identical(
self.dataset, **parameters)
self.assertEqual(
xp.max(p_m[self.dataset["mass_2"] <= parameters["mmin"]]), 0.0)
def test_iid_matches_independent_magnitudes(self):
iid_params = self.prior.sample()
ind_params = dict()
ind_params.update({key + '_1': iid_params[key] for key in iid_params})
ind_params.update({key + '_2': iid_params[key] for key in iid_params})
self.assertEquals(0.0, xp.max(
spin.iid_spin_magnitude_beta(self.test_data, **iid_params) -
spin.independent_spin_magnitude_beta(
self.test_data, **ind_params)))
def test_iid_matches_independent(self):
params = self.prior.sample()
mag_params = {key: params[key] for key in ['amax', 'alpha_chi', 'beta_chi']}
tilt_params = {key: params[key] for key in ['xi_spin', 'sigma_spin']}
self.assertEquals(0.0, xp.max(
spin.iid_spin(self.test_data, **params) -
spin.iid_spin_magnitude_beta(self.test_data, **mag_params) *
spin.iid_spin_orientation_gaussian_isotropic(
self.test_data, **tilt_params)))
def _run_model_normalisation(self, model, priors):
norms = list()
for _ in range(self.n_test):
p_z = model(self.test_data, **priors.sample())
norms.append(trapz(p_z, self.zs))
self.assertAlmostEqual(xp.max(xp.abs(xp.asarray(norms) - 1)), 0.0)
def test_double_power_law_zero_below_mmin(self):
for ii in range(self.n_test):
parameters = self.power_prior.sample()
del parameters["beta"]
p_m = mass.double_power_law_primary_mass(self.m1s, **parameters)
self.assertEqual(xp.max(p_m[self.m1s <= parameters["mmin"]]), 0.0)
def _max_abs_difference(array, comparison):
return float(xp.max(xp.abs(comparison - xp.asarray(array))))
def test_power_law_primary_mass_ratio_zero_below_mmin(self):
m2s = self.dataset['mass_1'] * self.dataset['mass_ratio']
for ii in range(self.n_test):
parameters = self.power_prior.sample()
p_m = mass.power_law_primary_mass_ratio(self.dataset, **parameters)
self.assertEqual(xp.max(p_m[m2s <= parameters['mmin']]), 0.0)
