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 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 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,
)
)
)