Merge branch '182-inconsistent-saved-log_likelihood-values' into 'master'

Resolve "Inconsistent saved `log_likelihood` values"

Closes #182

See merge request Monash/tupak!208

CHANGELOG.md

@@ -19,6 +19,7 @@ Changes currently on master, but not under a tag.
 - Adds plotting of the prior on 1D marginal distributions of corner plots
 - Adds a method to plot time-domain GW data
 - Hyperparameter estimation now enables the user to provide the single event evidences
+- Add nested samples to nestle output
 - Prior and child classes now implement the \_\_eq\_\_ magic method for comparisons
 
 ### Changes
@@ -34,6 +35,7 @@ Changes currently on master, but not under a tag.
 re-instantiate the Prior in most cases
 - Users can now choose to overwrite existing result files, rather than creating
   a .old file.
+- Make likelihood values stored in the posterior correct for dynesty and nestle
 
 ### Removed
 - Removes the "--detectors" command line argument (not a general CLI requirement)

tupak/core/sampler/dynesty.py

@@ -131,14 +131,16 @@ class Dynesty(Sampler):
 
         # self.result.sampler_output = out
         weights = np.exp(out['logwt'] - out['logz'][-1])
-        self.result.samples = dynesty.utils.resample_equal(
-            out.samples, weights)
-        self.result.log_likelihood_evaluations = out.logl
-        self.result.log_evidence = out.logz[-1]
-        self.result.log_evidence_err = out.logzerr[-1]
+        self.result.samples = dynesty.utils.resample_equal(out.samples, weights)
         self.result.nested_samples = DataFrame(
             out.samples, columns=self.search_parameter_keys)
         self.result.nested_samples['weights'] = weights
+        self.result.nested_samples['log_likelihood'] = out.logl
+        idxs = [np.unique(np.where(self.result.samples[ii] == out.samples)[0])
+                for ii in range(len(out.logl))]
+        self.result.log_likelihood_evaluations = out.logl[idxs]
+        self.result.log_evidence = out.logz[-1]
+        self.result.log_evidence_err = out.logzerr[-1]
 
         if self.plot:
             self.generate_trace_plots(out)

tupak/core/sampler/nestle.py

 import numpy as np
+from pandas import DataFrame
 from .base_sampler import Sampler
 
 
@@ -69,7 +70,13 @@ class Nestle(Sampler):
 
         self.result.sampler_output = out
         self.result.samples = nestle.resample_equal(out.samples, out.weights)
-        self.result.log_likelihood_evaluations = out.logl
+        self.result.nested_samples = DataFrame(
+            out.samples, columns=self.search_parameter_keys)
+        self.result.nested_samples['weights'] = out.weights
+        self.result.nested_samples['log_likelihood'] = out.logl
+        idxs = [np.unique(np.where(self.result.samples[ii] == out.samples)[0])
+                for ii in range(len(out.logl))]
+        self.result.log_likelihood_evaluations = out.logl[idxs]
         self.result.log_evidence = out.logz
         self.result.log_evidence_err = out.logzerr
         return self.result