def test_gaussian_chi_p(self):
self.assertTrue(
xp.all(
spin.gaussian_chi_p(
dict(chi_p=xp.linspace(-2, 2, 1001)), mu_chi_p=0.4, sigma_chi_p=0.1
)
== truncnorm(xp.linspace(-2, 2, 1001), mu=0.4, sigma=0.1, low=0, high=1)
)
)
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 test_2d_gaussian_normalized(self):
model = spin.GaussianChiEffChiP()
chi_eff, chi_p = xp.meshgrid(xp.linspace(-1, 1, 501), xp.linspace(0, 1, 300))
parameters = dict(
mu_chi_eff=0.1,
mu_chi_p=0.3,
sigma_chi_eff=0.6,
sigma_chi_p=0.5,
spin_covariance=0.9,
)
prob = model(dict(chi_eff=chi_eff, chi_p=chi_p), **parameters)
self.assertAlmostEqual(
xp.trapz(xp.trapz(prob, xp.linspace(-1, 1, 501)), xp.linspace(0, 1, 300)),
1,
5,
)
def test_2d_gaussian_no_covariance_matches_independent(self):
model = spin.GaussianChiEffChiP()
data = dict(chi_eff=xp.linspace(-2, 2, 1001),
chi_p=xp.linspace(0, 2, 1001))
self.assertTrue(
xp.all(
spin.gaussian_chi_eff(data, mu_chi_eff=0.4, sigma_chi_eff=0.1)
* spin.gaussian_chi_p(data, mu_chi_p=0.4, sigma_chi_p=0.1) ==
model(
data,
mu_chi_eff=0.4,
mu_chi_p=0.4,
sigma_chi_eff=0.1,
sigma_chi_p=0.1,
spin_covariance=0.0,
)))
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 test_2d_gaussian_no_covariance_matches_independent(self):
model = spin.GaussianChiEffChiP()
chi_eff, chi_p = xp.meshgrid(xp.linspace(-1, 1, 501), xp.linspace(0, 1, 300))
data = dict(chi_eff=chi_eff, chi_p=chi_p)
self.assertTrue(
xp.all(
spin.gaussian_chi_eff(data, mu_chi_eff=0.6, sigma_chi_eff=0.2)
* spin.gaussian_chi_p(data, mu_chi_p=0.4, sigma_chi_p=0.1)
== model(
data,
mu_chi_eff=0.6,
mu_chi_p=0.4,
sigma_chi_eff=0.2,
sigma_chi_p=0.1,
spin_covariance=0.0,
)
)
)
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 __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 setUp(self):
model = lambda x: xp.ones_like(x['a'])
data = dict(a=xp.linspace(0, 1, 1000), vt=2)
self.n_test = 100
self.vt = vt.GridVT(model, data)
def setUp(self):
self.zs = xp.linspace(1e-3, 1, 1000)
self.test_data = dict(redshift=self.zs)
self.n_test = 100
def setUp(self) -> None:
model = (lambda dataset: xp.exp(-((dataset["a"] - 0.5)**2) / 2) /
(2 * xp.pi)**0.5)
self.data = dict(a=xp.linspace(0, 1, 1000), prior=xp.ones(1000), vt=2)
self.vt = vt.ResamplingVT(data=self.data, model=model, n_events=0)
