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")
# compare directly with getdist
mcmc_gd = getdist.loadMCSamples(file_root="./tests/example_data/cb")
assert_array_almost_equal(mcmc.logL, mcmc_gd.loglikes, decimal=15)
def test_read_getdist():
np.random.seed(3)
mcmc = MCMCSamples(root='./tests/example_data/gd')
mcmc.plot_2d(['x0', 'x1', 'x2', 'x3'])
mcmc.plot_1d(['x0', 'x1', 'x2', 'x3'])
mcmc = MCMCSamples(root='./tests/example_data/gd_single')
mcmc.plot_2d(['x0', 'x1', 'x2', 'x3'])
mcmc.plot_1d(['x0', 'x1', 'x2', 'x3'])
plt.close("all")
def test_read_getdist_discard_burn_in():
np.random.seed(3)
mcmc = MCMCSamples(burn_in=0.3, root='./tests/example_data/gd')
mcmc.plot_2d(['x0', 'x1', 'x2', 'x3'])
mcmc.plot_1d(['x0', 'x1', 'x2', 'x3'])
# for 2 getdist chains of length 5000
mcmc0 = MCMCSamples(root='./tests/example_data/gd')
mcmc1 = MCMCSamples(burn_in=1000, root='./tests/example_data/gd')
for key in ['x0', 'x1', 'x2', 'x3', 'x4']:
assert_array_equal(mcmc0[key][1000:5000], mcmc1[key][:4000])
mcmc1.plot_2d(['x0', 'x1', 'x2', 'x3', 'x4'])
mcmc1.plot_1d(['x0', 'x1', 'x2', 'x3', 'x4'])
def test_plotting_with_integer_names():
np.random.seed(3)
samples_1 = MCMCSamples(data=np.random.rand(1000, 3))
samples_2 = MCMCSamples(data=np.random.rand(1000, 3))
fig, ax = samples_1.plot_2d([0, 1, 2])
samples_2.plot_2d(ax)
fig, ax = samples_1.plot_1d([0, 1, 2])
samples_2.plot_1d(ax)
assert samples_1[0].shape == (1000,)
assert_array_equal(samples_1.loc[:, 0], samples_1[0])
assert_array_equal(samples_1.loc[:, 0], samples_1.iloc[:, 0])
with pytest.raises(KeyError):
samples_1['0']
def test_discard_burn_in(root):
np.random.seed(3)
mcmc = MCMCSamples(burn_in=0.3, root='./tests/example_data/' + root)
mcmc.plot_2d(['x0', 'x1', 'x2', 'x3'])
mcmc.plot_1d(['x0', 'x1', 'x2', 'x3'])
# for 2 chains of length 1000
mcmc0 = MCMCSamples(root='./tests/example_data/' + root)
mcmc1 = MCMCSamples(burn_in=1000, root='./tests/example_data/' + root)
for key in ['x0', 'x1', 'x2', 'x3', 'x4']:
if key in mcmc0:
assert key in mcmc1
assert_array_equal(mcmc0[key][1000:2000], mcmc1[key][:1000])
mcmc1.plot_2d(['x0', 'x1', 'x2', 'x3', 'x4'])
mcmc1.plot_1d(['x0', 'x1', 'x2', 'x3', 'x4'])
def test_plot_1d_colours():
np.random.seed(3)
gd = MCMCSamples(root="./tests/example_data/gd")
gd.drop(columns='x3', inplace=True)
pc = NestedSamples(root="./tests/example_data/pc")
pc.drop(columns='x4', inplace=True)
mn = NestedSamples(root="./tests/example_data/mn")
mn.drop(columns='x2', inplace=True)
plot_types = ['kde', 'hist']
if 'astropy' in sys.modules:
plot_types += ['astropyhist']
if 'fastkde' in sys.modules:
plot_types += ['fastkde']
for plot_type in plot_types:
fig = plt.figure()
fig, axes = make_1d_axes(['x0', 'x1', 'x2', 'x3', 'x4'], fig=fig)
gd.plot_1d(axes, plot_type=plot_type, label="gd")
pc.plot_1d(axes, plot_type=plot_type, label="pc")
mn.plot_1d(axes, plot_type=plot_type, label="mn")
gd_colors = []
pc_colors = []
mn_colors = []
for x, ax in axes.iteritems():
handles, labels = ax.get_legend_handles_labels()
for handle, label in zip(handles, labels):
if isinstance(handle, Rectangle):
color = to_hex(handle.get_facecolor())
else:
color = handle.get_color()
if label == 'gd':
gd_colors.append(color)
elif label == 'pc':
pc_colors.append(color)
elif label == 'mn':
mn_colors.append(color)
assert(len(set(gd_colors)) == 1)
assert(len(set(mn_colors)) == 1)
assert(len(set(pc_colors)) == 1)
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")
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")
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',
'diagonal': 'kde'
})
#| ... triangle plots (with the equivalent scatter plot filling up the left hand side) ...