def test_manual_columns():
old_params = ['x0', 'x1', 'x2', 'x3', 'x4']
mcmc_params = ['logL']
ns_params = ['logL', 'logL_birth', 'nlive']
mcmc = MCMCSamples(root='./tests/example_data/gd')
ns = NestedSamples(root='./tests/example_data/pc')
assert_array_equal(mcmc.columns, old_params + mcmc_params)
assert_array_equal(ns.columns, old_params + ns_params)
new_params = ['y0', 'y1', 'y2', 'y3', 'y4']
mcmc = MCMCSamples(root='./tests/example_data/gd', columns=new_params)
ns = NestedSamples(root='./tests/example_data/pc', columns=new_params)
assert_array_equal(mcmc.columns, new_params + mcmc_params)
assert_array_equal(ns.columns, new_params + ns_params)
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_weighted_merging():
# Generate some data to try it out:
samples_1 = NestedSamples(root='./tests/example_data/pc')
samples_2 = NestedSamples(root='./tests/example_data/pc_250')
samples_1['xtest'] = 7*samples_1['x3']
samples_2['xtest'] = samples_2['x3']
mean1 = samples_1.mean()['xtest']
mean2 = samples_2.mean()['xtest']
# Test with evidence weights
weight1 = np.exp(samples_1.logZ())
weight2 = np.exp(samples_2.logZ())
samples = merge_samples_weighted([samples_1, samples_2])
mean = samples.mean()['xtest']
assert np.isclose(mean, (mean1*weight1+mean2*weight2)/(weight1+weight2))
# Test with explicit weights
weight1 = 31
weight2 = 13
samples = merge_samples_weighted(
[samples_1, samples_2], weights=[weight1, weight2])
mean = samples.mean()['xtest']
assert np.isclose(mean, (mean1*weight1+mean2*weight2)/(weight1+weight2))
# Test if correct exceptions are raised:
# MCMCSamples are passed without weights
with pytest.raises(ValueError):
merge_samples_weighted([MCMCSamples(samples_1)])
# len(weights) != len(samples)
with pytest.raises(ValueError):
merge_samples_weighted([samples_1, samples_2], weights=[1, 2, 3])
# A samples is passed and not a sequence
with pytest.raises(TypeError):
merge_samples_weighted(samples_1, weights=[1, 2, 3])
def test_root_and_label():
np.random.seed(3)
ns = NestedSamples(root='./tests/example_data/pc')
assert(ns.root == './tests/example_data/pc')
assert(ns.label == 'pc')
ns = NestedSamples()
assert(ns.root is None)
assert(ns.label is None)
mc = MCMCSamples(root='./tests/example_data/gd')
assert (mc.root == './tests/example_data/gd')
assert(mc.label == 'gd')
mc = MCMCSamples()
assert(mc.root is None)
assert(mc.label is None)
def test_to_getdist():
try:
anesthetic_samples = MCMCSamples(root='./tests/example_data/gd')
getdist_samples = to_getdist(anesthetic_samples)
assert_array_equal(getdist_samples.samples, anesthetic_samples)
assert_array_equal(getdist_samples.weights, anesthetic_samples.weights)
for param, p in zip(getdist_samples.getParamNames().names,
anesthetic_samples.columns):
assert param.name == p
except ModuleNotFoundError:
pass
def test_weighted_merging():
# Generate some data to try it out:
samples_1 = NestedSamples(root='./tests/example_data/pc')
samples_2 = NestedSamples(root='./tests/example_data/pc_250')
samples_1['xtest'] = 7*samples_1['x3']
samples_2['xtest'] = samples_2['x3']
samples_1.tex['xtest'] = "$x_{t,1}$"
samples_2.tex['xtest'] = "$x_{t,2}$"
mean1 = samples_1.mean()['xtest']
mean2 = samples_2.mean()['xtest']
# Test with evidence weights
weight1 = np.exp(samples_1.logZ())
weight2 = np.exp(samples_2.logZ())
samples = merge_samples_weighted([samples_1, samples_2],
label='Merged label')
mean = samples.mean()['xtest']
assert np.isclose(mean, (mean1*weight1+mean2*weight2)/(weight1+weight2))
# Test tex and label
for key in samples.keys():
if key in samples_2.keys():
assert samples.tex[key] == samples_2.tex[key]
else:
assert samples.tex[key] == samples_1.tex[key]
assert samples.label == 'Merged label'
# Test that label is None when no label is passed
samples_1.label = "1"
samples_2.label = "2"
samples = merge_samples_weighted([samples_1, samples_2])
assert samples.label is None
# Test with explicit weights
weight1 = 31
weight2 = 13
samples = merge_samples_weighted(
[samples_1, samples_2], weights=[weight1, weight2])
mean = samples.mean()['xtest']
assert np.isclose(mean, (mean1*weight1+mean2*weight2)/(weight1+weight2))
# Test if correct exceptions are raised:
# MCMCSamples are passed without weights
with pytest.raises(ValueError):
merge_samples_weighted([MCMCSamples(samples_1)])
# len(weights) != len(samples)
with pytest.raises(ValueError):
merge_samples_weighted([samples_1, samples_2], weights=[1, 2, 3])
# A samples is passed and not a sequence
with pytest.raises(TypeError):
merge_samples_weighted(samples_1, weights=[1, 2, 3])
def test_MCMCSamples_importance_sample():
np.random.seed(3)
mc0 = MCMCSamples(root='./tests/example_data/gd')
with pytest.raises(NotImplementedError):
mc0.importance_sample(mc0.logL, action='spam')
mc_masked = mc0.importance_sample(mc0.logL, action='replace')
assert_array_equal(mc0.logL, mc_masked.logL)
assert_array_equal(mc0.weights, mc_masked.weights)
mc_masked = mc0.importance_sample(np.zeros_like(mc0.logL), action='add')
assert_array_equal(mc0.logL, mc_masked.logL)
assert_array_equal(mc0.weights, mc_masked.weights)
mask = ((mc0.x0 > -0.3) & (mc0.x2 > 0.2) & (mc0.x4 < 3.5)).to_numpy()
mc_masked = mc0[mask]
mc1 = mc0.importance_sample(mask, action='mask')
assert_array_equal(mc_masked.logL, mc1.logL)
assert_array_equal(mc_masked.weights, mc1.weights)
assert mc0.tex == mc1.tex
assert mc0.limits == mc1.limits
assert mc0.root == mc1.root
assert mc0.label == mc1.label
assert mc1._metadata == mc0._metadata
assert mc0 is not mc1
assert mc0.tex is not mc1.tex
assert mc0.limits is not mc1.limits
mc0.importance_sample(mask, action='mask', inplace=True)
assert type(mc0) is MCMCSamples
assert_array_equal(mc0, mc1)
assert mc0.tex == mc1.tex
assert mc0.limits == mc1.limits
assert mc0.root == mc1.root
assert mc0.label == mc1.label
assert mc1._metadata == mc0._metadata
assert mc0 is not mc1
assert mc0.tex is not mc1.tex
assert mc0.limits is not mc1.limits
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_build_mcmc():
numpy.random.seed(3)
nsamps = 1000
ndims = 3
samples = numpy.random.randn(nsamps, ndims)
logL = numpy.random.rand(nsamps)
w = numpy.random.randint(1, 20, size=nsamps)
params = ['A', 'B', 'C']
tex = {'A': '$A$', 'B': '$B$', 'C': '$C$'}
limits = {'A': (-1, 1), 'B': (-2, 2), 'C': (-3, 3)}
mcmc = MCMCSamples(data=samples)
assert (len(mcmc) == nsamps)
assert_array_equal(mcmc.columns, numpy.array([0, 1, 2], dtype=object))
mcmc = MCMCSamples(data=samples, logL=logL)
assert (len(mcmc) == nsamps)
assert_array_equal(mcmc.columns,
numpy.array([0, 1, 2, 'logL'], dtype=object))
mcmc = MCMCSamples(data=samples, w=w)
assert (len(mcmc) == nsamps)
assert_array_equal(mcmc.columns,
numpy.array([0, 1, 2, 'weight'], dtype=object))
mcmc = MCMCSamples(data=samples, w=w, logL=logL)
assert (len(mcmc) == nsamps)
assert_array_equal(mcmc.columns,
numpy.array([0, 1, 2, 'logL', 'weight'], dtype=object))
mcmc = MCMCSamples(data=samples, columns=params)
assert (len(mcmc) == nsamps)
assert_array_equal(mcmc.columns, ['A', 'B', 'C'])
mcmc = MCMCSamples(data=samples, tex=tex)
for p in params:
assert (mcmc.tex[p] == tex[p])
mcmc = MCMCSamples(data=samples, limits=limits)
for p in params:
assert (mcmc.limits[p] == limits[p])
assert (mcmc.root is None)
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_MCMCSamples_importance_sample():
np.random.seed(3)
mc0 = MCMCSamples(root='./tests/example_data/gd')
with pytest.raises(NotImplementedError):
mc0.importance_sample(mc0.logL, action='spam')
# new gaussian logL
logL_i = norm.logpdf(mc0.x3, loc=0.4, scale=0.1)
# add logL
mc1 = mc0.importance_sample(np.zeros_like(mc0.logL), action='add')
assert_array_equal(mc0.logL, mc1.logL)
assert_array_equal(mc0.weights, mc1.weights)
mc1 = mc0.importance_sample(logL_new=logL_i)
assert np.all(mc1.logL.to_numpy() != mc0.logL.to_numpy())
assert not np.all(mc1.weights == mc0.weights)
# replace logL
mc2 = mc0.importance_sample(mc0.logL, action='replace')
assert_array_equal(mc0.logL, mc2.logL)
assert_array_equal(mc0.weights, mc2.weights)
mc2 = mc0.importance_sample(mc0.logL.to_numpy()+logL_i, action='replace')
assert np.all(mc2.logL.to_numpy() != mc0.logL.to_numpy())
assert not np.all(mc2.weights == mc0.weights)
assert_array_equal(mc1.logL.to_numpy(), mc2.logL.to_numpy())
assert_array_almost_equal(mc1.logL.to_numpy(), mc2.logL.to_numpy())
# mask logL
mask = ((mc0.x0 > -0.3) & (mc0.x2 > 0.2) & (mc0.x4 < 3.5)).to_numpy()
mc_masked = mc0[mask]
mc3 = mc0.importance_sample(mask, action='mask')
assert_array_equal(mc_masked.logL, mc3.logL)
assert_array_equal(mc_masked.weights, mc3.weights)
assert np.all(mc3.x0 > -0.3)
for mc in [mc1, mc2, mc3]:
assert mc.tex == mc0.tex
assert mc.limits == mc0.limits
assert mc.root == mc0.root
assert mc.label == mc0.label
assert mc._metadata == mc0._metadata
assert mc is not mc0
assert mc.tex is not mc0.tex
assert mc.limits is not mc0.limits
mc0.importance_sample(mask, action='mask', inplace=True)
assert type(mc0) is MCMCSamples
assert_array_equal(mc3, mc0)
assert mc3.tex == mc0.tex
assert mc3.limits == mc0.limits
assert mc3.root == mc0.root
assert mc3.label == mc0.label
assert mc3._metadata == mc0._metadata
assert mc3 is not mc0
assert mc3.tex is not mc0.tex
assert mc3.limits is not mc0.limits
"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',
def test_plot_2d_legend():
np.random.seed(3)
ns = NestedSamples(root='./tests/example_data/pc')
mc = MCMCSamples(root='./tests/example_data/gd')
params = ['x0', 'x1', 'x2', 'x3']
# Test label kwarg for kde
fig, axes = make_2d_axes(params, upper=False)
ns.plot_2d(axes, label='l1', types=dict(diagonal='kde', lower='kde'))
mc.plot_2d(axes, label='l2', types=dict(diagonal='kde', lower='kde'))
for y, row in axes.iterrows():
for x, ax in row.iteritems():
if ax is not None:
handles, labels = ax.get_legend_handles_labels()
assert(labels == ['l1', 'l2'])
if x == y:
assert(all([isinstance(h, Line2D) for h in handles]))
else:
assert(all([isinstance(h, Rectangle) for h in handles]))
plt.close('all')
# Test label kwarg for hist and scatter
fig, axes = make_2d_axes(params, lower=False)
ns.plot_2d(axes, label='l1', types=dict(diagonal='hist', upper='scatter'))
mc.plot_2d(axes, label='l2', types=dict(diagonal='hist', upper='scatter'))
for y, row in axes.iterrows():
for x, ax in row.iteritems():
if ax is not None:
handles, labels = ax.get_legend_handles_labels()
assert(labels == ['l1', 'l2'])
if x == y:
assert(all([isinstance(h, Rectangle) for h in handles]))
else:
assert(all([isinstance(h, Line2D)
for h in handles]))
plt.close('all')
# test default labelling
fig, axes = make_2d_axes(params, upper=False)
ns.plot_2d(axes)
mc.plot_2d(axes)
for y, row in axes.iterrows():
for x, ax in row.iteritems():
if ax is not None:
handles, labels = ax.get_legend_handles_labels()
assert(labels == ['pc', 'gd'])
plt.close('all')
# Test label kwarg to constructors
ns = NestedSamples(root='./tests/example_data/pc', label='l1')
mc = MCMCSamples(root='./tests/example_data/gd', label='l2')
params = ['x0', 'x1', 'x2', 'x3']
fig, axes = make_2d_axes(params, upper=False)
ns.plot_2d(axes)
mc.plot_2d(axes)
for y, row in axes.iterrows():
for x, ax in row.iteritems():
if ax is not None:
handles, labels = ax.get_legend_handles_labels()
assert(labels == ['l1', 'l2'])
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")
def test_build_mcmc():
np.random.seed(3)
nsamps = 1000
ndims = 3
data = np.random.randn(nsamps, ndims)
logL = np.random.rand(nsamps)
weights = np.random.randint(1, 20, size=nsamps)
params = ['A', 'B', 'C']
tex = {'A': '$A$', 'B': '$B$', 'C': '$C$'}
limits = {'A': (-1, 1), 'B': (-2, 2), 'C': (-3, 3)}
mcmc = MCMCSamples(data=data)
assert(len(mcmc) == nsamps)
assert_array_equal(mcmc.columns, np.array([0, 1, 2], dtype=object))
mcmc = MCMCSamples(data=data, logL=logL)
assert(len(mcmc) == nsamps)
assert_array_equal(mcmc.columns, np.array([0, 1, 2, 'logL'], dtype=object))
mcmc = MCMCSamples(data=data, weights=weights)
assert(len(mcmc) == nsamps)
assert_array_equal(mcmc.columns, np.array([0, 1, 2], dtype=object))
assert(mcmc.index.nlevels == 2)
mcmc = MCMCSamples(data=data, weights=weights, logL=logL)
assert(len(mcmc) == nsamps)
assert_array_equal(mcmc.columns, np.array([0, 1, 2, 'logL'], dtype=object))
assert(mcmc.index.nlevels == 2)
mcmc = MCMCSamples(data=data, columns=params)
assert(len(mcmc) == nsamps)
assert_array_equal(mcmc.columns, ['A', 'B', 'C'])
mcmc = MCMCSamples(data=data, tex=tex)
for p in params:
assert(mcmc.tex[p] == tex[p])
mcmc = MCMCSamples(data=data, limits=limits)
for p in params:
assert(mcmc.limits[p] == limits[p])
ns = NestedSamples(data=data, logL=logL, weights=weights)
assert(len(ns) == nsamps)
assert(np.all(np.isfinite(ns.logL)))
logL[:10] = -1e300
weights[:10] = 0.
mcmc = MCMCSamples(data=data, logL=logL, weights=weights, logzero=-1e29)
ns = NestedSamples(data=data, logL=logL, weights=weights, logzero=-1e29)
assert_array_equal(mcmc.columns, np.array([0, 1, 2, 'logL'], dtype=object))
assert(mcmc.index.nlevels == 2)
assert_array_equal(ns.columns, np.array([0, 1, 2, 'logL'], dtype=object))
assert(ns.index.nlevels == 2)
assert(np.all(mcmc.logL[:10] == -np.inf))
assert(np.all(ns.logL[:10] == -np.inf))
assert(np.all(mcmc.logL[10:] == logL[10:]))
assert(np.all(ns.logL[10:] == logL[10:]))
mcmc = MCMCSamples(data=data, logL=logL, weights=weights, logzero=-1e301)
ns = NestedSamples(data=data, logL=logL, weights=weights, logzero=-1e301)
assert(np.all(np.isfinite(mcmc.logL)))
assert(np.all(np.isfinite(ns.logL)))
assert(np.all(mcmc.logL == logL))
assert(np.all(ns.logL == logL))
assert(mcmc.root is None)
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_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_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_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_read_fail():
with pytest.raises(FileNotFoundError):
MCMCSamples(root='./tests/example_data/foo')
x1 = x[-1] + np.random.randn(ndims) * 0.1
l1 = loglikelihood(x1)
if np.random.rand() < np.exp(l1 - l[-1]):
x.append(x1)
l.append(l1)
w.append(1)
else:
w[-1] += 1
return np.array(x), np.array(l), np.array(w)
np.random.seed(0)
data, logL, weights = mcmc_sim()
mcmc = MCMCSamples(data=data,
columns=columns,
logL=logL,
weights=weights,
tex=tex)
mcmc['minuslogL'] = -mcmc.logL
mcmc['weight'] = mcmc.weights
# MCMC multiple files
root = './tests/example_data/gd'
roots.append(root)
mcmc[['weight', 'minuslogL'] + columns][:len(mcmc) // 2].to_csv(root +
'_1.txt',
sep=' ',
index=False,
header=False)
mcmc[['weight', 'minuslogL'] + columns][len(mcmc) // 2:].to_csv(root +
'_2.txt',
while len(x) < 10000:
x1 = x[-1] + np.random.randn(ndims) * 0.1
l1 = loglikelihood(x1)
if np.random.rand() < np.exp(l1 - l[-1]):
x.append(x1)
l.append(l1)
w.append(1)
else:
w[-1] += 1
return np.array(x), np.array(l), np.array(w)
np.random.seed(0)
data, logL, w = mcmc_sim()
mcmc = MCMCSamples(data=data, columns=columns, logL=logL, w=w, tex=tex)
mcmc['chi2'] = -2 * mcmc.logL
# MCMC multiple files
root = './tests/example_data/gd'
roots.append(root)
mcmc[['weight', 'chi2'] + columns][:len(mcmc) // 2].to_csv(root + '_1.txt',
sep=' ',
index=False,
header=False)
mcmc[['weight', 'chi2'] + columns][len(mcmc) // 2:].to_csv(root + '_2.txt',
sep=' ',
index=False,
header=False)
# MCMC single file
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")
import tarfile
for filename in ["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:
%matplotlib notebook
from anesthetic import MCMCSamples
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) ...