From bbffcf308d82549c11dbe1f2eaea61f5fc59ba90 Mon Sep 17 00:00:00 2001
From: Matthew Pitkin <matthew.pitkin@ligo.org>
Date: Mon, 1 Apr 2019 17:03:42 +0100
Subject: [PATCH] Start attempt at adding an add method to results to combine
samples
---
bilby/core/result.py | 34 ++++++++++++++++++++++++++++--
bilby/core/sampler/base_sampler.py | 2 +-
bilby/core/sampler/cpnest.py | 1 +
3 files changed, 34 insertions(+), 3 deletions(-)
diff --git a/bilby/core/result.py b/bilby/core/result.py
index aaf0a81df..94b247a4f 100644
--- a/bilby/core/result.py
+++ b/bilby/core/result.py
@@ -98,7 +98,7 @@ class Result(object):
log_evidence_err=np.nan, log_noise_evidence=np.nan,
log_bayes_factor=np.nan, log_likelihood_evaluations=None,
log_prior_evaluations=None, sampling_time=None, nburn=None,
- walkers=None, max_autocorrelation_time=None,
+ walkers=None, max_autocorrelation_time=None, use_ratio=None,
parameter_labels=None, parameter_labels_with_unit=None,
gzip=False, version=None):
""" A class to store the results of the sampling run
@@ -137,6 +137,9 @@ class Result(object):
The samplers taken by a ensemble MCMC samplers
max_autocorrelation_time: float
The estimated maximum autocorrelation time for MCMC samplers
+ use_ratio: bool
+ A boolean stating whether the likelihood ratio, as opposed to the
+ likelihood was used during sampling
parameter_labels, parameter_labels_with_unit: list
Lists of the latex-formatted parameter labels
gzip: bool
@@ -169,6 +172,7 @@ class Result(object):
self.nested_samples = nested_samples
self.walkers = walkers
self.nburn = nburn
+ self.use_ratio = use_ratio
self.log_evidence = log_evidence
self.log_evidence_err = log_evidence_err
self.log_noise_evidence = log_noise_evidence
@@ -388,7 +392,7 @@ class Result(object):
'log_noise_evidence', 'log_bayes_factor', 'priors', 'posterior',
'injection_parameters', 'meta_data', 'search_parameter_keys',
'fixed_parameter_keys', 'constraint_parameter_keys',
- 'sampling_time', 'sampler_kwargs',
+ 'sampling_time', 'sampler_kwargs', 'use_ratio',
'log_likelihood_evaluations', 'log_prior_evaluations', 'samples',
'nested_samples', 'walkers', 'nburn', 'parameter_labels',
'parameter_labels_with_unit', 'version']
@@ -1167,6 +1171,32 @@ class Result(object):
"keyword argument, e.g. " + caller_func.__name__ + "(outdir='.')")
return outdir
+ def __add__(self, other):
+ """
+ Method to add two Results objects.
+ """
+
+ if not isinstance(other, Result):
+ raise TypeError("Trying to add a non-Result object to a Result "
+ "object")
+
+ # check that the results have some common features
+
+ # check parameters are the same
+ if not set(self.search_parameter_keys) == set(other.search_parameter_keys):
+ raise ValueError("Results being added contain inconsistent parameters")
+
+ if self.log_noise_evidence != other.log_noise_evidence:
+ raise ValueError("Results being added do not have consistent "
+ "noise evidences")
+
+ # check priors are the same
+ if self.priors is not None and other.priors is not None:
+ for p in self.search_parameter_keys:
+ if not self.priors[p] == other.priors[p]:
+ raise ValueError("Results being added used inconsistent "
+ "priors")
+
def plot_multiple(results, filename=None, labels=None, colours=None,
save=True, evidences=False, **kwargs):
diff --git a/bilby/core/sampler/base_sampler.py b/bilby/core/sampler/base_sampler.py
index daa204293..da9dd6e02 100644
--- a/bilby/core/sampler/base_sampler.py
+++ b/bilby/core/sampler/base_sampler.py
@@ -213,7 +213,7 @@ class Sampler(object):
constraint_parameter_keys=self._constraint_keys,
priors=self.priors, meta_data=self.meta_data,
injection_parameters=self.injection_parameters,
- sampler_kwargs=self.kwargs)
+ sampler_kwargs=self.kwargs, use_ratio=self.use_ratio)
if result_class is None:
result = Result(**result_kwargs)
diff --git a/bilby/core/sampler/cpnest.py b/bilby/core/sampler/cpnest.py
index eec05efc3..45f87ba6c 100644
--- a/bilby/core/sampler/cpnest.py
+++ b/bilby/core/sampler/cpnest.py
@@ -86,6 +86,7 @@ class Cpnest(NestedSampler):
out.plot()
self.result.posterior = DataFrame(out.posterior_samples)
+ self.result.nested_samples = DataFrame(out.get_nested_samples(filename=None))
self.result.posterior.rename(columns=dict(
logL='log_likelihood', logPrior='log_prior'), inplace=True)
self.result.log_evidence = out.NS.state.logZ
--
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