def plot_chain(self,
chain,
save_figure=False,
prefix=None,
extension='pdf'):
"""
Produces a triangle plot from a chain, which can be
saved to file automatically.
Args:
chain (array): 2D array with shape [n_samples,n_params],
where `n_samples` is the number of samples in the
chain and `n_params` is the number of free parameters
for this likelihood run.
save_figures (bool): if true, figures will be saved to
file. File names will take the form:
<`prefix`>triangle.<`extension`>
prefix (str): output prefix.
extension (str): plot extension (pdf, pdf etc.).
Returns:
figure object
"""
from getdist import MCSamples
from getdist import plots as gplots
nsamples = len(chain)
# Generate samples
ranges = {}
for n, pr in zip(self.p_free_names, self.p_free_prior):
if pr['type'] == 'TopHat':
ranges[n] = pr['values']
samples = MCSamples(samples=chain[nsamples // 4:],
names=self.p_free_names,
labels=self.p_free_labels,
ranges=ranges)
samples.smooth_scale_2D = 0.2
# Triangle plot
g = gplots.getSubplotPlotter()
g.triangle_plot([samples], filled=True)
if save_figure:
if prefix is None:
prefix = self.prefix_out
fname = prefix + 'triangle.' + extension
g.export(fname)
return g
def plot_chain(self,
chain,
taus=None,
pars=None,
save_figure=False,
prefix=None,
extension='pdf'):
"""
Produces a triangle plot from a chain, which can be
saved to file automatically.
Args:
chain (array): 2D array with shape [n_samples,n_params],
where `n_samples` is the number of samples in the
chain and `n_params` is the number of free parameters
for this likelihood run.
taus (array): array of length n_params, that contains
the autocorrelation times for the input chains.
which_pars (str or list): which parameters to plot
save_figures (bool): if true, figures will be saved to
file. File names will take the form:
<`prefix`>triangle.<`extension`>
prefix (str): output prefix.
extension (str): plot extension (pdf, pdf etc.).
Returns:
figure object
"""
from getdist import MCSamples
from getdist import plots as gplots
nsamples = len(chain)
# Generate samples
ranges = {}
for n, pr in zip(self.p_free_names, self.p_free_prior):
if pr['type'] == 'TopHat':
ranges[n] = pr['values']
if taus is None:
chain_eff = chain[nsamples // 4:]
else:
chain_eff = chain[2 * int(np.ceil(taus.max())):]
print("Removed 2x max chain autocorrelation time.")
if pars is None:
names_eff = self.p_free_names
labels_eff = self.p_free_labels
ranges_eff = ranges
else: # triangle plot with only some parameters
if type(pars) != list:
pars = [pars]
indices = [self.p_free_names.index(par) for par in pars]
chain_eff = chain[:, indices]
names_eff = pars
labels_eff = [self.p_free_labels[idx] for idx in indices]
ranges_eff = {par: ranges[par] for par in pars}
samples = MCSamples(samples=chain_eff,
names=names_eff,
labels=labels_eff,
ranges=ranges_eff)
samples.smooth_scale_2D = 0.2
# Triangle plot
g = gplots.getSubplotPlotter()
g.triangle_plot([samples], filled=True)
if save_figure:
if prefix is None:
raise ValueError("Need a file prefix to save stuff")
if pars is None:
fname = prefix + 'triangle.' + extension
else:
fname = prefix + "triangle_" + "_".join(pars) + "." + extension
g.export(fname)
return g
