From b2c04968acde412874fe89476f41622711883b19 Mon Sep 17 00:00:00 2001
From: Matthew Pitkin <matthew.pitkin@ligo.org>
Date: Tue, 2 Apr 2019 14:11:20 +0100
Subject: [PATCH] flake8 fixes
---
bilby/core/result.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/bilby/core/result.py b/bilby/core/result.py
index 0522b62be..379308248 100644
--- a/bilby/core/result.py
+++ b/bilby/core/result.py
@@ -3,6 +3,7 @@ from __future__ import division
import os
from distutils.version import LooseVersion
from collections import OrderedDict, namedtuple
+from functools import reduce
import numpy as np
import pandas as pd
@@ -1305,7 +1306,6 @@ class ResultList(object):
# get evidences and weights
log_evs = np.array([res.log_evidence for res in self])
- log_weights = [np.log(res.nested_samples['weights']) for res in self]
# average the evidence for each run
log_evidence = reduce(np.logaddexp, log_evs) - np.log(len(self))
@@ -1317,7 +1317,7 @@ class ResultList(object):
result.log_bayes_factor = log_evidence - result.log_noise_evidence
# add errors in quadrature
- log_errs = [res.log_evidence_err for res in self is np.isfinite(res.log_evidence_err)]
+ log_errs = [res.log_evidence_err for res in self if np.isfinite(res.log_evidence_err)]
if len(log_errs) > 0:
log_err = 2. * log_errs[0]
for err in log_errs[1:]:
@@ -1330,7 +1330,7 @@ class ResultList(object):
fracs = [n / np.max(nlives) for n in Ns] # number of samples from each Result
# select samples from the individual posteriors
- posts = [res.posterior[np.random.uniform(size=len(post)) < frac] for res, frac in zip(self, fracs)]
+ posts = [res.posterior[np.random.uniform(size=len(res.posterior)) < frac] for res, frac in zip(self, fracs)]
# remove original nested_samples
result.nested_samples = None
--
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