def test_NS_input_fails_in_MCMCSamples():
with pytest.raises(ValueError) as excinfo:
MCMCSamples(root='./tests/example_data/pc')
assert "Please use NestedSamples instead which has the same features as " \
"MCMCSamples and more. MCMCSamples should be used for MCMC " \
"chains only." in str(excinfo.value)
def test_read_cobayamcmc():
np.random.seed(3)
mcmc = MCMCSamples(root='./tests/example_data/cb')
mcmc.plot_2d(['x0', 'x1'])
mcmc.plot_1d(['x0', 'x1'])
plt.close("all")
def test_read_montepython():
np.random.seed(3)
mcmc = MCMCSamples(root='./tests/example_data/mp')
mcmc.plot_2d(['x0', 'x1', 'x2', 'x3'])
mcmc.plot_1d(['x0', 'x1', 'x2', 'x3'])
plt.close("all")
"plikHM_TTTEEE_lowl_lowE_lensing.tar.gz",
"plikHM_TTTEEE_lowl_lowE_lensing_NS.tar.gz"
]:
github_url = "https://github.com/williamjameshandley/cosmo_example/raw/master/"
url = github_url + filename
open(filename, 'wb').write(requests.get(url).content)
tarfile.open(filename).extractall()
#| ## Marginalised posterior plotting
#| Import anesthetic and load the MCMC samples:
import matplotlib.pyplot as plt
from anesthetic import MCMCSamples, make_2d_axes
mcmc_root = 'plikHM_TTTEEE_lowl_lowE_lensing/base_plikHM_TTTEEE_lowl_lowE_lensing'
mcmc = MCMCSamples(root=mcmc_root)
#| We have plotting tools for 1D plots ...
fig, axes = mcmc.plot_1d('omegabh2')
#| ... multiple 1D plots ...
fig, axes = mcmc.plot_1d(['omegabh2', 'omegach2', 'H0', 'tau', 'logA', 'ns'])
fig.tight_layout()
#| ... triangle plots ...
mcmc.plot_2d(['omegabh2', 'omegach2', 'H0'],
types={
'lower': 'kde',