from .likelihood import Likelihood
import copy
import time
import numpy as np
import pickle
[docs]class Posterior(Likelihood):
"""Posterior object
Posterior object to be sent to the fitting routines.
It is essentially the same as the Liklihood object,
but priors are applied here.
Args:
likelihood (radvel.likelihood.Likelihood): Likelihood object
params (radvel.model.Parameters): parameters object
Note:
Append `radvel.prior.Prior` objects to the Posterior.priors list
to apply priors in the likelihood calculations.
"""
def __init__(self,likelihood):
self.likelihood = likelihood
self.params = likelihood.params
self.uparams = likelihood.uparams
self.priors = []
def __repr__(self):
s = super(Posterior, self).__repr__()
s += "\nPriors\n"
s += "------\n"
for prior in self.priors:
s += prior.__repr__() + "\n"
return s
[docs] def logprob(self):
"""Log probability
Log-probability for the likelihood given the list
of priors in `Posterior.priors`.
Returns:
float: log probability of the likelihood + priors
"""
_logprob = self.likelihood.logprob()
for prior in self.priors:
_logprob += prior( self.params )
return _logprob
[docs] def bic(self):
"""
Calculate the Bayesian information criterion
Returns:
float: BIC
"""
n = len(self.likelihood.y)
k = len(self.likelihood.get_vary_params())
_bic = np.log(n) * k - 2.0 * self.logprob()
return _bic
[docs] def logprob_array(self, param_values_array):
"""Log probability for parameter vector
Same as `self.logprob`, but will take a vector of
parameter values. Useful as the objective function
for routines that optimize a vector of parameter values
instead of the dictionary-like format of the `radvel.model.Parameters` object.
Returns:
float: log probability of the likelihood + priors
"""
self.likelihood.set_vary_params(param_values_array)
_logprob = self.logprob()
return _logprob
[docs] def writeto(self, filename):
"""
Save posterior object to pickle file.
Args:
filename (string): full path to outputfile
"""
with open(filename, 'wb') as f:
pickle.dump(self, f)
[docs]def load(filename):
"""
Load posterior object from pickle file.
Args:
filename (string): full path to pickle file
"""
with open(filename, 'rb') as f:
post = pickle.load(f)
for key,val in post.params.items():
if val is None:
del post.params[key]
return post