def test_raise(self):
self.sigma_Dd = 100
self.sigma_Ddt = 100
num_samples = 100
cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Ob0=0.05)
z_L = 0.3
z_S = 2
lensCosmo = LensCosmo(z_L, z_S, cosmo=cosmo)
self.Dd_true = lensCosmo.D_d
self.D_dt_true = lensCosmo.D_dt
D_dt_samples = np.random.normal(self.D_dt_true, self.sigma_Ddt, num_samples)
D_d_samples = np.random.normal(self.Dd_true, self.sigma_Dd, num_samples)
self.cosmoL = CosmoLikelihood(z_L, z_S, D_d_samples, D_dt_samples, sampling_option="H0_only", omega_m_fixed=0.3,
omega_lambda_fixed=0.7, omega_mh2_fixed=0.14157, kde_type='scipy_gaussian',
bandwidth=1, flat=True)
self.cosmoL.sampling_option = 'WRONG'
with self.assertRaises(ValueError):
self.cosmoL(a=[])
with self.assertRaises(ValueError):
self.cosmoL.likelihood(a=[])
with self.assertRaises(ValueError):
self.cosmoL.computeLikelihood(ctx=[])
with self.assertRaises(ValueError):
param = CosmoParam(sampling_option='WRONG')
param.numParam
with self.assertRaises(ValueError):
param = CosmoParam(sampling_option='WRONG')
param.param_bounds
def setup(self):
self.sigma_Dd = 100
self.sigma_Ddt = 100
num_samples = 100
cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Ob0=0.05)
z_L = 0.3
z_S = 2
lensCosmo = LensCosmo(z_L, z_S, cosmo=cosmo)
self.Dd_true = lensCosmo.D_d
self.D_dt_true = lensCosmo.D_dt
D_dt_samples = np.random.normal(self.D_dt_true, self.sigma_Ddt,
num_samples)
D_d_samples = np.random.normal(self.Dd_true, self.sigma_Dd,
num_samples)
self.cosmoL = CosmoLikelihood(z_L,
z_S,
D_d_samples,
D_dt_samples,
sampling_option="H0_only",
omega_m_fixed=0.3,
omega_lambda_fixed=0.7,
omega_mh2_fixed=0.14157,
kde_type='scipy_gaussian',
bandwidth=1,
flat=True)
class TestCosmoLikelihood(object):
def setup(self):
self.sigma_Dd = 100
self.sigma_Ddt = 100
num_samples = 100
cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Ob0=0.05)
z_L = 0.3
z_S = 2
lensCosmo = LensCosmo(z_L, z_S, cosmo=cosmo)
self.Dd_true = lensCosmo.D_d
self.D_dt_true = lensCosmo.D_dt
D_dt_samples = np.random.normal(self.D_dt_true, self.sigma_Ddt, num_samples)
D_d_samples = np.random.normal(self.Dd_true, self.sigma_Dd, num_samples)
self.cosmoL = CosmoLikelihood(z_L, z_S, D_d_samples, D_dt_samples, sampling_option="H0_only", omega_m_fixed=0.3,
omega_lambda_fixed=0.7, omega_mh2_fixed=0.14157, kde_type='scipy_gaussian',
bandwidth=1, flat=True)
def test_prior_H0(self):
logL, bool = CosmoLikelihood.prior_H0(H0=10, H0_min=50, H0_max=100)
assert bool is False
def test_prior_omega_mh2(self):
prior, bool = self.cosmoL.prior_omega_mh2(h=1, omega_m=2, h_max=2)
assert bool is False
def test_prior_omega_m(self):
penalty, bool = self.cosmoL.prior_omega_m(omega_m=0, omega_m_min=0.05, omega_m_max=1)
assert bool is False
def test_call(self):
self.cosmoL.sampling_option = 'H0_only'
a = [70]
logL, _ = self.cosmoL(a)
npt.assert_almost_equal(logL, -11, decimal=-1)
self.cosmoL.sampling_option = 'H0_omega_m'
a = [70, 0.3]
logL, _ = self.cosmoL(a)
npt.assert_almost_equal(logL, -11, decimal=-1)
self.cosmoL.sampling_option = "fix_omega_mh2"
a = [70]
logL, _ = self.cosmoL(a)
npt.assert_almost_equal(logL, -11, decimal=-1)
self.cosmoL.sampling_option = 'H0_omega_m_omega_de'
a = [70, 0.3, 0.7]
logL, _ = self.cosmoL(a)
npt.assert_almost_equal(logL, -11, decimal=-1)
def test_prior_H0(self):
logL, bool = CosmoLikelihood.prior_H0(H0=10, H0_min=50, H0_max=100)
assert bool is False