def second_generation_mass_spin(
dataset,
alpha,
beta,
mmin,
mmax,
lam,
mpp,
sigpp,
):
second_generation_mass = two_component_primary_mass_ratio(
dataset=dataset,
alpha=alpha,
beta=beta * 4,
mmin=mmin * 2,
mmax=mmax * 2,
lam=lam,
mpp=mpp * 2,
sigpp=sigpp * 2,
)
alpha_2g, beta_2g, _ = mu_chi_var_chi_max_to_alpha_beta_max(mu_chi=0.67,
var_chi=0.01,
amax=1)
second_generation_spin = iid_spin_magnitude_beta(dataset=dataset,
alpha_chi=alpha_2g,
beta_chi=beta_2g,
amax=1)
return second_generation_mass * second_generation_spin
def test_returns_zero_alpha_beta_less_zero(self):
parameters = self.prior.sample()
for key in ["alpha_chi", "beta_chi"]:
parameters[key] = -1
self.assertEqual(
spin.iid_spin_magnitude_beta(self.test_data, **parameters), 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_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 generate_population_prior(population_params):
params = DEFAULT_POPULATION_PARAMS.copy()
params.update(population_params)
p, _ = convert_to_beta_parameters(params)
# make grid_x-vals
num_x = 10000
mass = np.linspace(5, 100, num=num_x)
q = np.linspace(0, 1, num=num_x)
cos_vals = np.linspace(-1, 1, num=num_x)
a = np.linspace(0, 1, num=num_x)
z = np.linspace(0, 2.3, num=num_x)
# calcualte probabilites
mass_model = SinglePeakSmoothedMassDistribution()
p_mass = mass_model.p_m1(
dataset=pd.DataFrame(dict(mass_1=mass)),
alpha=p["alpha"],
mmin=p["mmin"],
mmax=p["mmax"],
lam=p["lam"],
mpp=p["mpp"],
sigpp=p["sigpp"],
delta_m=p["delta_m"],
)
p_q = mass_model.p_q(
dataset=pd.DataFrame(dict(mass_ratio=q, mass_1=mass)),
beta=p["beta"],
mmin=p["mmin"],
delta_m=p["delta_m"],
)
p_costheta12 = truncnorm(xx=cos_vals,
mu=1,
sigma=p["sigma_12"],
high=1,
low=-1)
p_costilt1 = truncnorm(xx=cos_vals,
mu=1,
sigma=p["sigma_1"],
high=1,
low=-1)
p_a = iid_spin_magnitude_beta(
dataset=pd.DataFrame(dict(a_1=a, a_2=a)),
amax=p["amax"],
alpha_chi=p["alpha_chi"],
beta_chi=p["beta_chi"],
)
p_z = PowerLawRedshift(z_max=2.3).probability(dataset=pd.DataFrame(
dict(redshift=z)),
lamb=p["lamb"])
# after generating prior, generate samples, then convert the samples to BBH params
priors = bilby.prior.PriorDict(
dict(
a_1=Interped(a,
p_a,
minimum=0,
maximum=1,
name="a_1",
latex_label="$a_1$"),
a_2=Interped(a,
p_a,
minimum=0,
maximum=1,
name="a_2",
latex_label="$a_2$"),
redshift=Interped(
z,
p_z,
minimum=0,
maximum=2.3,
name="redshift",
latex_label="$pred_z$",
),
cos_tilt_1=Interped(
cos_vals,
p_costilt1,
minimum=-1,
maximum=1,
name="cos_tilt_1",
latex_label="$\\cos\ \\mathrm{tilt}_1$",
),
cos_theta_12=Interped(
cos_vals,
p_costheta12,
minimum=-1,
maximum=1,
name="cos_theta_12",
latex_label="$\\cos\ \\theta_{12}$",
),
mass_1_source=Interped(
mass,
p_mass,
minimum=5,
maximum=100,
name="mass_1_source",
latex_label="$m_{1}$",
),
mass_ratio=Interped(
q,
p_q,
minimum=0,
maximum=1,
name="mass_ratio",
latex_label="$q$",
),
dec=Cosine(name="dec"),
ra=Uniform(name="ra",
minimum=0,
maximum=2 * np.pi,
boundary="periodic"),
psi=Uniform(name="psi",
minimum=0,
maximum=np.pi,
boundary="periodic"),
phi_1=Uniform(
name="phi_1",
minimum=0,
maximum=2 * np.pi,
boundary="periodic",
latex_label="$\\phi_1$",
),
phi_12=Uniform(
name="phi_z_s12",
minimum=0,
maximum=2 * np.pi,
boundary="periodic",
latex_label="$\\phi_{12}$",
),
phase=Uniform(name="phase",
minimum=0,
maximum=2 * np.pi,
boundary="periodic"),
incl=Uniform(name="incl",
minimum=0,
maximum=2 * np.pi,
boundary="periodic"),
geocent_time=Uniform(
minimum=-0.1,
maximum=0.1,
name="geocent_time",
latex_label="$t_c$",
unit="$s$",
),
))
return priors