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_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 __init__(self):
self.retention_file = self.__retention_file__()
branching_dataset = np.load(
self.retention_file,
allow_pickle=True,
encoding="latin1",
)
self.a_1_array = xp.asarray(branching_dataset["a1"])
self.a_2_array = xp.asarray(branching_dataset["a2"])
self.mass_ratio_array = xp.asarray(branching_dataset["q"])
self.retention_fraction = xp.asarray(
branching_dataset["interpolated_retention_fraction"])
self.mass_1s = xp.linspace(2, 200, 2000)
self.mass_ratio_grid, self.mass_1_grid = xp.meshgrid(
self.mass_ratio_array, self.mass_1s)
self.first_generation_data = dict(mass_1=self.mass_1_grid,
mass_ratio=self.mass_ratio_grid)
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 low_spin_component_big_grid(spin, spin_array):
delta = xp.asarray(spin == 0).astype(float)
delta_norm = xp.asarray(spin_array == 0).astype(float)
return delta / trapz(delta_norm, spin_array)
def low_spin_component_grid(spin):
delta = xp.asarray(spin == 0).astype(float)
return delta / trapz(delta, spin)
def low_spin_component(spin):
return xp.asarray(spin == 0).astype(float)
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 _max_abs_difference(array, comparison):
return float(xp.max(xp.abs(comparison - xp.asarray(array))))
