def corner_plot(self, **kwargs):
"""
Produce the corner plot showing the marginal distributions in one and two directions.
:param kwargs: arguments to be passed to the corner function
:return: a matplotlib.figure instance
"""
if self.samples is not None:
assert len(self._free_parameters.keys()) == self.raw_samples[0].shape[0], ("Mismatch between sample"
" dimensions and number of free"
" parameters")
labels = []
priors = []
for i, (parameter_name, parameter) in enumerate(self._free_parameters.iteritems()):
short_name = parameter_name.split(".")[-1]
labels.append(short_name)
priors.append(self._likelihood_model.parameters[parameter_name].prior)
fig = corner(self.raw_samples, labels=labels,
quantiles=[0.16, 0.50, 0.84],
priors=priors, **kwargs)
return fig
else:
raise RuntimeError("You have to run the sampler first, using the sample() method")
def cornerPlot( self, **kwargs ):
if hasattr( self, "samples" ):
labels = []
priors = []
for i,( srcName, paramName ) in enumerate( self.freeParameters.keys() ):
thisLabel = "%s of %s" % ( paramName, srcName )
labels.append( thisLabel )
priors.append( self.likelihoodModel.parameters[ srcName ][ paramName ].prior )
fig = corner( self.samples, labels=labels,
quantiles=[0.16, 0.50, 0.84],
priors = priors, **kwargs )
return fig
else:
raise RuntimeError("You have to run the sampler first, using the sample() method")
