def test_different_parameters():
np.random.seed(3)
params_x = ['x0', 'x1', 'x2', 'x3', 'x4']
params_y = ['x0', 'x1', 'x2']
fig, axes = make_1d_axes(params_x)
ns = NestedSamples(root='./tests/example_data/pc')
ns.plot_1d(axes)
fig, axes = make_2d_axes(params_y)
ns.plot_2d(axes)
fig, axes = make_2d_axes(params_x)
ns.plot_2d(axes)
fig, axes = make_2d_axes([params_x, params_y])
ns.plot_2d(axes)
plt.close('all')
def test_masking():
pc = NestedSamples(root="./tests/example_data/pc")
mask = pc['x0'] > 0
plot_types = ['kde', 'hist']
if 'fastkde' in sys.modules:
plot_types += ['fastkde']
for ptype in plot_types:
fig, axes = make_1d_axes(['x0', 'x1', 'x2'])
pc[mask].plot_1d(axes=axes, plot_type=ptype)
for ptype in plot_types + ['scatter']:
fig, axes = make_2d_axes(['x0', 'x1', 'x2'], upper=False)
pc[mask].plot_2d(axes=axes, types=dict(lower=ptype, diagonal='hist'))
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")
ns_output[:6]
#| The evidence, KL divergence and Bayesian model dimensionality, with their corresponding errors, are:
for x in ns_output:
print('%10s = %9.2f +/- %4.2f' %
(x, ns_output[x].mean(), ns_output[x].std()))
#| Since ``ns_output`` is a set of ``MCMCSamples``, it may be plotted as usual.
#| Here we illustrate slightly more fine-grained control of the axes construction
#| (demanding three columns)
from anesthetic import make_1d_axes
fig, axes = make_1d_axes(['logZ', 'D', 'd'], ncols=3, tex=ns_output.tex)
ns_output.plot_1d(axes)
#| We can also inspect the correlation between these inferences:
ns_output.plot_2d(['logZ', 'D'])
#| Here is a comparison of the base and NS output
h = nested['H0'] / 100
nested['omegab'] = nested['omegabh2'] / h**2
nested.tex['omegab'] = '$\Omega_b$'
fig, axes = mcmc.plot_2d(['sigma8', 'omegab'])
nested.plot_2d(axes=axes)
