def _load__txt(self, skip=0):
self.log.debug("Skipping %d rows", skip)
self._data = load_DataFrame(self.file_name,
skip=skip,
root_file_name=self.root_file_name)
self.log.info("Loaded %d sample points from '%s'", len(self._data),
self.file_name)
def plot_progress(progress,
ax=None,
index=None,
figure_kwargs=empty_dict,
legend_kwargs=empty_dict):
"""
Plots progress of one or more MCMC runs: evolution of R-1
(for means and c.l. intervals) and acceptance rate.
Takes a ``progress`` instance (actually a ``pandas.DataFrame``,
returned as part of the sampler ``products``),
a chain ``output`` prefix, or a list of any of those
for plotting progress of several chains at once.
You can use ``figure_kwargs`` and ``legend_kwargs`` to pass arguments to
``matplotlib.pyplot.figure`` and ``matplotlib.pyplot.legend`` respectively.
Return a subplots axes array. Display with ``matplotlib.pyplot.show()``.
"""
if ax is None:
import matplotlib.pyplot as plt
# noinspection PyTypeChecker
fig, ax = plt.subplots(nrows=2, sharex=True, **figure_kwargs)
if isinstance(progress, DataFrame):
pass # go on to plotting
elif isinstance(progress, str):
try:
if not progress.endswith(Extension.progress):
progress += Extension.progress
progress = load_DataFrame(progress)
# 1-based
progress.index = np.arange(1, len(progress) + 1)
except:
raise ValueError("Cannot load progress file %r" % progress)
elif hasattr(type(progress), "__iter__"):
# Assume is a list of progress'es
for i, p in enumerate(progress):
plot_progress(p, ax=ax, index=i + 1)
return ax
else:
raise ValueError("Cannot understand progress argument: %r" % progress)
# Plot!
tag_pre = "" if index is None else "%d : " % index
p = ax[0].semilogy(progress.N,
progress.Rminus1,
"o-",
label=tag_pre + "means")
ax[0].semilogy(progress.N,
progress.Rminus1_cl,
"x:",
c=p[0].get_color(),
label=tag_pre + "bounds")
ax[0].set_ylabel(r"$R-1$")
ax[0].legend(**legend_kwargs)
ax[1].plot(progress.N, progress.acceptance_rate, "o-")
ax[1].set_ylabel(r"acc. rate")
return ax
def _load__txt(self, skip=0, thin=1):
self.log.debug("Skipping %d rows and thinning with factor %d.", skip, thin)
self._data = load_DataFrame(self.file_name, skip=skip, thin=thin)
self.log.info("Loaded %d samples from '%s'", len(self._data), self.file_name)