Add nested samples to multinest result

bilby/core/sampler/pymultinest.py

@@ -237,6 +237,7 @@ class Pymultinest(NestedSampler):
         self.calc_likelihood_count()
         self.result.outputfiles_basename = self.outputfiles_basename
         self.result.sampling_time = datetime.timedelta(seconds=self.total_sampling_time)
+        self.result.nested_samples = self._nested_samples
         return self.result
 
     def _check_and_load_sampling_time_file(self):
@@ -259,3 +260,27 @@ class Pymultinest(NestedSampler):
         if self.use_temporary_directory:
             self._move_temporary_directory_to_proper_path()
             self.kwargs["outputfiles_basename"] = self.outputfiles_basename
+
+    @property
+    def _nested_samples(self):
+        """
+        Extract nested samples from the pymultinest files.
+        This requires combining the "dead" points from `ev.dat` and the "live"
+        points from `phys_live.points`.
+        The prior volume associated with the current live points is the simple
+        estimate of `remaining_prior_volume / N`.
+        """
+        import pandas as pd
+        dir_ = self.kwargs["outputfiles_basename"]
+        dead_points = np.genfromtxt(dir_ + "/ev.dat")
+        live_points = np.genfromtxt(dir_ + "/phys_live.points")
+
+        nlive = self.kwargs["n_live_points"]
+        final_log_prior_volume = - len(dead_points) / nlive - np.log(nlive)
+        live_points = np.insert(live_points, -1, final_log_prior_volume, axis=-1)
+
+        nested_samples = pd.DataFrame(
+            np.vstack([dead_points, live_points]).copy(),
+            columns=self.search_parameter_keys + ["log_likelihood", "log_prior_volume", "mode"]
+        )
+        return nested_samples