isochrones/starmodel_old.py
def prop_samples(self,prop,return_values=True,conf=0.683):
        """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)
        
        """
        samples = self.samples[prop].values
        
        if return_values:
            sorted = np.sort(samples)
            med = np.median(samples)
            n = len(samples)
            lo_ind = int(n*(0.5 - conf/2))
            hi_ind = int(n*(0.5 + conf/2))
            lo = med - sorted[lo_ind]
            hi = sorted[hi_ind] - med
            return samples, (med,lo,hi)
        else:
            return samples
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)
        
        
Returns samples of given property, based on MCMC sampling
Returns samples of given property based on MCMC sampling
def prop_samples(self,prop,return_values=True,conf=0.683):
        samples = self.samples[prop].values
        if return_values:
            sorted = np.sort(samples)
            med = np.median(samples)
            n = len(samples)
            lo_ind = int(n*(0.5 - conf/2))
            hi_ind = int(n*(0.5 + conf/2))
            lo = med - sorted[lo_ind]
            hi = sorted[hi_ind] - med
            return samples, (med,lo,hi)
        else:
            return samples
def prop_samples ( self , prop , return_values = True , conf = 0.683 ) : samples = self . samples [ prop ] . values if return_values : sorted = np . sort ( samples ) med = np . median ( samples ) n = len ( samples ) lo_ind = int ( n * ( 0.5 - conf / 2 ) ) hi_ind = int ( n * ( 0.5 + conf / 2 ) ) lo = med - sorted [ lo_ind ] hi = sorted [ hi_ind ] - med return samples , ( med , lo , hi ) else : return samples
Returns samples of given property, based on MCMC sampling
Returns samples of given property based on MCMC sampling
"""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)
        
        """
:class:`np.ndarray` of desired samples
: class : ` np . ndarray ` of desired samples
Optionally also return summary statistics (median, lo_err, hi_err),
if ``returns_values == True`` (this is default behavior)
Optionally also return summary statistics ( median lo_err hi_err ) if ` ` returns_values == True ` ` ( this is default behavior )
self
prop
Name of desired property.  Must be column of ``self.samples``.
Name of desired property . Must be column of ` ` self . samples ` ` .
return_values
(optional)
If ``True`` (default), then also return (median, lo_err, hi_err)
corresponding to desired credible interval.
( optional ) If ` ` True ` ` ( default ) then also return ( median lo_err hi_err ) corresponding to desired credible interval .
conf
(optional)
Desired quantile for credible interval.
( optional ) Desired quantile for credible interval .