def prop_samples(self,prop,return_values=True,conf=<NUM_LIT>):
samples = self.samples[prop].values<EOL>if return_values:<EOL><INDENT>sorted = np.sort(samples)<EOL>med = np.median(samples)<EOL>n = len(samples)<EOL>lo_ind = int(n*(<NUM_LIT:0.5> - conf/<NUM_LIT:2>))<EOL>hi_ind = int(n*(<NUM_LIT:0.5> + conf/<NUM_LIT:2>))<EOL>lo = med - sorted[lo_ind]<EOL>hi = sorted[hi_ind] - med<EOL>return samples, (med,lo,hi)<EOL><DEDENT>else:<EOL><INDENT>return samples<EOL><DEDENT>
Returns samples of given property, based on MCMC sampling

        :param prop:
            Name of desired property.  Must be column of ``self.samples``.

        :param return_values: (optional)
            If ``True`` (default), then also return (median, lo_err, hi_err)
            corresponding to desired credible interval.

        :param conf: (optional)
            Desired quantile for credible interval.  Default = 0.683.

        :return:
            :class:`np.ndarray` of desired samples

        :return: 
            Optionally also return summary statistics (median, lo_err, hi_err),
            if ``returns_values == True`` (this is default behavior)