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