From 7f9f9cf31036944274f44a1b571e39b5477e10be Mon Sep 17 00:00:00 2001
From: Sylvia Biscoveanu <sylvia.biscoveanu@ligo.org>
Date: Mon, 29 Nov 2021 18:08:22 +0000
Subject: [PATCH] Change using_mpi test
---
bilby/core/sampler/pymultinest.py | 33 ++++++++++++++++++++-----------
1 file changed, 21 insertions(+), 12 deletions(-)
diff --git a/bilby/core/sampler/pymultinest.py b/bilby/core/sampler/pymultinest.py
index 91356d146..d98693622 100644
--- a/bilby/core/sampler/pymultinest.py
+++ b/bilby/core/sampler/pymultinest.py
@@ -79,6 +79,12 @@ class Pymultinest(NestedSampler):
temporary_directory=True,
**kwargs
):
+ try:
+ from mpi4py import MPI
+
+ using_mpi = MPI.COMM_WORLD.Get_size() > 1
+ except ImportError:
+ using_mpi = False
super(Pymultinest, self).__init__(
likelihood=likelihood,
priors=priors,
@@ -92,7 +98,6 @@ class Pymultinest(NestedSampler):
)
self._apply_multinest_boundaries()
self.exit_code = exit_code
- using_mpi = len([key for key in os.environ if "MPI" in key])
if using_mpi and temporary_directory:
logger.info(
"Temporary directory incompatible with MPI, "
@@ -111,15 +116,15 @@ class Pymultinest(NestedSampler):
kwargs["n_live_points"] = kwargs.pop(equiv)
def _verify_kwargs_against_default_kwargs(self):
- """ Check the kwargs """
+ """Check the kwargs"""
self.outputfiles_basename = self.kwargs.pop("outputfiles_basename", None)
# for PyMultiNest >=2.9 the n_params kwarg cannot be None
if self.kwargs["n_params"] is None:
self.kwargs["n_params"] = self.ndim
- if self.kwargs['dump_callback'] is None:
- self.kwargs['dump_callback'] = self._dump_callback
+ if self.kwargs["dump_callback"] is None:
+ self.kwargs["dump_callback"] = self._dump_callback
NestedSampler._verify_kwargs_against_default_kwargs(self)
def _dump_callback(self, *args, **kwargs):
@@ -166,7 +171,7 @@ class Pymultinest(NestedSampler):
)
def write_current_state_and_exit(self, signum=None, frame=None):
- """ Write current state and exit on exit_code """
+ """Write current state and exit on exit_code"""
logger.info(
"Run interrupted by signal {}: checkpoint and exit on {}".format(
signum, self.exit_code
@@ -187,11 +192,13 @@ class Pymultinest(NestedSampler):
self.outputfiles_basename, self.temporary_outputfiles_basename
)
)
- if self.outputfiles_basename.endswith('/'):
+ if self.outputfiles_basename.endswith("/"):
outputfiles_basename_stripped = self.outputfiles_basename[:-1]
else:
outputfiles_basename_stripped = self.outputfiles_basename
- distutils.dir_util.copy_tree(self.temporary_outputfiles_basename, outputfiles_basename_stripped)
+ distutils.dir_util.copy_tree(
+ self.temporary_outputfiles_basename, outputfiles_basename_stripped
+ )
def _move_temporary_directory_to_proper_path(self):
"""
@@ -241,9 +248,9 @@ class Pymultinest(NestedSampler):
return self.result
def _check_and_load_sampling_time_file(self):
- self.time_file_path = self.kwargs["outputfiles_basename"] + '/sampling_time.dat'
+ self.time_file_path = self.kwargs["outputfiles_basename"] + "/sampling_time.dat"
if os.path.exists(self.time_file_path):
- with open(self.time_file_path, 'r') as time_file:
+ with open(self.time_file_path, "r") as time_file:
self.total_sampling_time = float(time_file.readline())
else:
self.total_sampling_time = 0
@@ -253,7 +260,7 @@ class Pymultinest(NestedSampler):
new_sampling_time = current_time - self.start_time
self.total_sampling_time += new_sampling_time
self.start_time = current_time
- with open(self.time_file_path, 'w') as time_file:
+ with open(self.time_file_path, "w") as time_file:
time_file.write(str(self.total_sampling_time))
def _clean_up_run_directory(self):
@@ -271,16 +278,18 @@ class Pymultinest(NestedSampler):
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)
+ 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"]
+ columns=self.search_parameter_keys
+ + ["log_likelihood", "log_prior_volume", "mode"],
)
return nested_samples
--
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