def setUp(self):
self.costilts = xp.linspace(-1, 1, 1000)
self.test_data = dict(
cos_tilt_1=xp.einsum("i,j->ij", self.costilts, xp.ones_like(self.costilts)),
cos_tilt_2=xp.einsum("i,j->ji", self.costilts, xp.ones_like(self.costilts)),
)
self.prior = PriorDict(dict(xi_spin=Uniform(0, 1), sigma_spin=Uniform(0, 4)))
self.n_test = 100
def setUp(self):
self.a_array = xp.linspace(0, 1, 1000)
self.test_data = dict(
a_1=xp.einsum('i,j->ij', self.a_array, xp.ones_like(self.a_array)),
a_2=xp.einsum('i,j->ji', self.a_array, xp.ones_like(self.a_array)))
self.prior = PriorDict(
dict(amax=Uniform(0.3, 1), alpha_chi=Uniform(1, 4),
beta_chi=Uniform(1, 4)))
self.n_test = 100
def setUp(self):
self.a_array = xp.linspace(0, 1, 1000)
self.costilts = xp.linspace(-1, 1, 1000)
self.test_data = dict(
a_1=xp.einsum("i,j->ij", self.a_array, xp.ones_like(self.a_array)),
a_2=xp.einsum("i,j->ji", self.a_array, xp.ones_like(self.a_array)),
cos_tilt_1=xp.einsum("i,j->ij", self.costilts, xp.ones_like(self.costilts)),
cos_tilt_2=xp.einsum("i,j->ji", self.costilts, xp.ones_like(self.costilts)),
)
self.prior = PriorDict(
dict(
amax=Uniform(0.3, 1),
alpha_chi=Uniform(1, 4),
beta_chi=Uniform(1, 4),
xi_spin=Uniform(0, 1),
sigma_spin=Uniform(0, 4),
)
)
self.n_test = 100
def compute_branching_ratio(
self,
alpha,
beta,
mmin,
mmax,
lam,
mpp,
sigpp,
alpha_chi,
beta_chi,
delta_chi,
a_max=1,
):
probability = xp.einsum(
"i,j,k->ijk",
self.first_generation_mass_ratio(
alpha=alpha,
beta=beta,
mmin=mmin,
mmax=mmax,
lam=lam,
mpp=mpp,
sigpp=sigpp,
),
first_generation_spin_magnitude_grid(
self.a_1_array,
alpha=alpha_chi,
beta=beta_chi,
delta=delta_chi,
a_max=a_max,
),
first_generation_spin_magnitude_grid(
self.a_2_array,
alpha=alpha_chi,
beta=beta_chi,
delta=delta_chi,
a_max=a_max,
),
)
branching_ratio = trapz(
trapz(
trapz(
probability * self.retention_fraction,
self.mass_ratio_array,
),
self.a_2_array,
),
self.a_1_array,
)
branching_ratio = min(branching_ratio, 1)
return branching_ratio
