BUGFIX: close pool for emcee saving

bilby/core/sampler/dnest4.py

@@ -2,7 +2,6 @@ import datetime
 import time
 
 import numpy as np
-import pandas as pd
 
 from ..utils import logger
 from .base_sampler import NestedSampler, _TemporaryFileSamplerMixin, signal_wrapper
@@ -150,7 +149,6 @@ class DNest4(_TemporaryFileSamplerMixin, NestedSampler):
         self.start_time = np.nan
         self.sampler = None
         self._information = np.nan
-        self._last_live_sample_info = np.nan
 
         # Get the estimates of the prior distributions' widths and centers.
         widths = []
@@ -229,22 +227,7 @@ class DNest4(_TemporaryFileSamplerMixin, NestedSampler):
         self.result.log_evidence = stats["log_Z"]
         self._information = stats["H"]
         self.result.log_evidence_err = np.sqrt(self._information / self.num_particles)
-
-        if self._backend == "memory":
-            self._last_live_sample_info = pd.DataFrame(
-                self.sampler.backend.sample_info[-1]
-            )
-            self.result.log_likelihood_evaluations = self._last_live_sample_info[
-                "log_likelihood"
-            ]
-            self.result.samples = np.array(self.sampler.backend.posterior_samples)
-        else:
-            sample_info_path = (
-                "./" + self.kwargs["outputfiles_basename"] + "/sample_info.txt"
-            )
-            sample_info = np.genfromtxt(sample_info_path, comments="#", names=True)
-            self.result.log_likelihood_evaluations = sample_info["log_likelihood"]
-            self.result.samples = np.array(self.sampler.backend.posterior_samples)
+        self.result.samples = np.array(self.sampler.backend.posterior_samples)
 
         self.result.sampler_output = out
         self.result.outputfiles_basename = self.outputfiles_basename

bilby/core/sampler/emcee.py

@@ -403,6 +403,7 @@ class Emcee(MCMCSampler):
         if self.verbose:
             iterator.close()
         self.write_current_state()
+        self._close_pool()
 
         self.result.sampler_output = np.nan
         self.calculate_autocorrelation(self.sampler.chain.reshape((-1, self.ndim)))

test/integration/sampler_run_test.py

@@ -134,14 +134,15 @@ class TestRunningSamplers(unittest.TestCase):
             likelihood=self.likelihood,
             priors=self.priors,
             sampler=sampler,
-            save=False,
+            save="hdf5",
             npool=pool_size,
             conversion_function=self.conversion_function,
             **kwargs,
             **extra_kwargs,
         )
         assert "derived" in res.posterior
-        assert res.log_likelihood_evaluations is not None
+        if sampler != "dnest4":
+            assert res.log_likelihood_evaluations is not None
 
     @parameterized.expand(_sampler_kwargs.keys())
     def test_interrupt_sampler_single(self, sampler):