def progress_report(self):
"""
Reports the progress of the inference
"""
self.get_intermediate_results()
dead_samples = self.intermediate_results['rejected_points']
live_samples = self.intermediate_results['live_points']
likelihood = self.intermediate_results['logLikelihood']
lnX = self.intermediate_results['lnX']
if not ((dead_samples is None) or (likelihood is None) or
(lnX is None)):
fig = visualization.trace_plot(
parameter_names=self._active_parameters,
samples=dead_samples,
live_samples=live_samples,
likelihood=likelihood,
lnX=lnX)
fig_filepath = os.path.join(self._run_directory,
'progress_report.pdf')
msg = 'Saving progress report to {}'.format(fig_filepath)
log.info(msg)
fig.savefig(fig_filepath)
if misc.is_notebook():
ipd.clear_output()
ipd.display(
ipd.Markdown(
"\n**Progress report:**"
'\nnumber of likelihood evaluations {}'.format(
self._likelihood_evaluations_counter)))
plt.show()
else:
print(msg)
def posterior_report(self, sdigits=2, **kwargs):
"""
Displays the best fit values and 1-sigma errors for each active
parameter. Also produces a corner plot of the samples, which is
saved to the run directory.
If running on a jupyter-notebook, a nice LaTeX display is used, and
the plot is shown.
Parameters
----------
sdigits : int
The number of significant digits to be used
"""
out = ''
for param, pdict in self.posterior_summary.items():
if misc.is_notebook():
# Extracts LaTeX representation from astropy unit object
out += r"\\ \text{{ {0}: }}\; ".format(param)
out += q2tex(*map(pdict.get, ['median', 'errup', 'errlo']),
sdigits=sdigits)
out += r"\\"
else:
out += r"{0}: ".format(param)
md, errlo, errup = map(pdict.get, ['median', 'errlo', 'errup'])
if isinstance(md, apu.Quantity):
unit = str(md.unit)
md, errlo, errup = map(lambda x: x.value,
[md, errlo, errup])
else:
unit = ""
v, l, u = misc.adjust_error_intervals(md,
errlo,
errup,
sdigits=sdigits)
out += r'{0} (-{1})/(+{2}) {3}\n'.format(v, l, u, unit)
fig = self.corner_plot(**kwargs)
fig.savefig(os.path.join(self._run_directory, 'corner_plot.pdf'))
if misc.is_notebook():
ipd.display(ipd.Markdown("\n**Posterior report:**"))
plt.show()
ipd.display(ipd.Math(out))
else:
# Restores linebreaks and prints
print('Posterior report')
print(out.replace(r'\n', '\n'))
def evidence_report(self, sdigits=4):
if misc.is_notebook():
ipd.display(ipd.Markdown("**Evidence report:**"))
out = r"\log\mathcal{{ Z }} = "
out += q2tex(self.log_evidence, self.log_evidence_err)
ipd.display(ipd.Math(out))
else:
print('Evidence report')
print('logZ =', self.log_evidence, '±', self.log_evidence_err)
def progress_report(self):
"""
Reports the progress of the inference
"""
# Try to call get_intermediate_results
try:
self.get_intermediate_results()
# If this method is not implemented, show error instead and continue
except NotImplementedError:
print("Progress reports can only be made if the "
"'get_intermediate_results()'-method is overridden! "
"Skipping report.")
# If this method is implemented, create progress report
else:
if mpirank!=0:
return
dead_samples = self.intermediate_results['rejected_points']
live_samples = self.intermediate_results['live_points']
likelihood = self.intermediate_results['logLikelihood']
lnX = self.intermediate_results['lnX']
if dead_samples is not None:
fig = visualization.trace_plot(
parameter_names=self._active_parameters,
samples=dead_samples,
live_samples=live_samples,
likelihood=likelihood, lnX=lnX)
fig_filepath = os.path.join(self._run_directory,
'progress_report.pdf')
msg = 'Saving progress report to {}'.format(fig_filepath)
log.info(msg)
fig.savefig(fig_filepath)
if misc.is_notebook():
ipd.clear_output()
ipd.display(ipd.Markdown(
"\n**Progress report:**\nnumber of likelihood "
"evaluations {}".format(
self._likelihood_evaluations_counter)))
plt.show()
else:
print(msg)