def test_sampling_H0_only(self):
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="H0_only",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2)
walkerRatio = 10
n_run = 10
n_burn = 10
mean_start = [self.H0_true]
sigma_start = [2]
mcmc_samples = mcmc_sampler.mcmc_CH(walkerRatio,
n_run,
n_burn,
mean_start,
sigma_start,
threadCount=1,
init_pos=None,
mpi_monch=False)
H0_mean = np.mean(mcmc_samples)
npt.assert_almost_equal(H0_mean / self.H0_true, 1, decimal=1)
sigma = np.sqrt(np.var(mcmc_samples))
npt.assert_almost_equal(sigma, 1.5, decimal=0)
def test_sampling_H0_omega_m_sklearn(self):
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="H0_omega_m",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2,
kde_type='gaussian',
bandwidth=10)
walkerRatio = 10
n_run = 10
n_burn = 10
mean_start = [self.H0_true, self.omega_m_true]
sigma_start = [5, 0.1]
mcmc_samples = mcmc_sampler.mcmc_CH(walkerRatio,
n_run,
n_burn,
mean_start,
sigma_start,
threadCount=1,
init_pos=None,
mpi_monch=False)
H0_mean = np.mean(mcmc_samples[:, 0])
npt.assert_almost_equal(H0_mean / self.H0_true, 1, decimal=1)
sigma = np.sqrt(np.var(mcmc_samples[:, 0]))
print(sigma)
npt.assert_almost_equal(sigma, 2.6, decimal=0)
def test_sampling_curvature(self):
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="H0_omega_m_omega_de",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2)
walkerRatio = 4
n_run = 10
n_burn = 10
mean_start = [self.H0_true, self.omega_m_true, 1 - self.omega_m_true]
sigma_start = [5, 0.1, 0.1]
mcmc_samples = mcmc_sampler.mcmc_CH(walkerRatio,
n_run,
n_burn,
mean_start,
sigma_start,
threadCount=1,
init_pos=None,
mpi_monch=False)
H0_mean = np.mean(mcmc_samples[:, 0])
npt.assert_almost_equal(H0_mean / self.H0_true, 1, decimal=1)
Om0_mean = np.mean(mcmc_samples[:, 1])
npt.assert_almost_equal(Om0_mean / self.omega_m_true, 1, decimal=0)
def test_sampling_H0_omega_m(self):
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="H0_omega_m",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2)
n_walkers = 10
n_run = 10
n_burn = 10
mean_start = [self.H0_true, self.omega_m_true]
sigma_start = [5, 0.1]
mcmc_samples = mcmc_sampler.mcmc_emcee(n_walkers, n_run, n_burn,
mean_start, sigma_start)
H0_mean = np.mean(mcmc_samples[:, 0])
npt.assert_almost_equal(H0_mean / self.H0_true, 1, decimal=1)
def test_sampling_fix_omega_mh2(self):
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="fix_omega_mh2",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2)
n_walkers = 10
n_run = 10
n_burn = 10
mean_start = [self.H0_true]
sigma_start = [2]
mcmc_samples = mcmc_sampler.mcmc_emcee(n_walkers, n_run, n_burn,
mean_start, sigma_start)
H0_mean = np.mean(mcmc_samples)
npt.assert_almost_equal(H0_mean / self.H0_true, 1, decimal=1)
sigma = np.sqrt(np.var(mcmc_samples))
npt.assert_almost_equal(sigma, 1.5, decimal=0)
def test_sampling_H0_omega_m_sklearn(self):
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="H0_omega_m",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2,
kde_type='gaussian',
bandwidth=10)
n_walkers = 10
n_run = 10
n_burn = 10
mean_start = [self.H0_true, self.omega_m_true]
sigma_start = [5, 0.1]
mcmc_samples = mcmc_sampler.mcmc_emcee(n_walkers, n_run, n_burn,
mean_start, sigma_start)
print(mcmc_samples, 'test samples')
H0_mean = np.mean(mcmc_samples[:, 0])
npt.assert_almost_equal(H0_mean / self.H0_true, 1, decimal=1)
sigma = np.sqrt(np.var(mcmc_samples[:, 0]))
print(sigma)
npt.assert_almost_equal(sigma, 2.6, decimal=0)
def test_mcmc_emcee(self):
n_walkers = 6
n_run = 2
n_burn = 2
mean_start = [self.H0_true]
sigma_start = [5]
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="H0_only",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2)
samples = mcmc_sampler.mcmc_emcee(n_walkers, n_run, n_burn, mean_start,
sigma_start)
assert len(samples) == n_walkers * n_run
mean_start = [self.H0_true, self.omega_m_true]
sigma_start = [5, 0.1]
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="H0_omega_m",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2)
samples = mcmc_sampler.mcmc_emcee(n_walkers, n_run, n_burn, mean_start,
sigma_start)
assert len(samples) == n_walkers * n_run
mean_start = [self.H0_true]
sigma_start = [5]
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="fix_omega_mh2",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2)
samples = mcmc_sampler.mcmc_emcee(n_walkers, n_run, n_burn, mean_start,
sigma_start)
assert len(samples) == n_walkers * n_run
mean_start = [self.H0_true, self.omega_m_true, 1 - self.omega_m_true]
sigma_start = [5, 0.1, 0.1]
mcmc_sampler = MCMCSampler(self.z_L,
self.z_S,
self.D_d_samples,
self.D_dt_samples,
sampling_option="H0_omega_m_omega_de",
omega_m_fixed=self.omega_m_true,
omega_mh2_fixed=self.omega_m_true *
(self.H0_true / 100)**2)
samples = mcmc_sampler.mcmc_emcee(n_walkers, n_run, n_burn, mean_start,
sigma_start)
assert len(samples) == n_walkers * n_run