From fe1b075d50e29bf61e5e60524d68eb531e468dc2 Mon Sep 17 00:00:00 2001
From: John Veitch <john.veitch@ligo.org>
Date: Thu, 16 Mar 2023 12:05:02 +0000
Subject: [PATCH] flake changes
---
bilby/core/result.py | 23 +++++++++++++----------
1 file changed, 13 insertions(+), 10 deletions(-)
diff --git a/bilby/core/result.py b/bilby/core/result.py
index bb895090a..7d22109e2 100644
--- a/bilby/core/result.py
+++ b/bilby/core/result.py
@@ -30,10 +30,12 @@ from .prior import Prior, PriorDict, DeltaFunction, ConditionalDeltaFunction
EXTENSIONS = ["json", "hdf5", "h5", "pickle", "pkl"]
+
def __eval_l(likelihood, params):
likelihood.parameters.update(params)
return likelihood.log_likelihood()
+
def result_file_name(outdir, label, extension='json', gzip=False):
""" Returns the standard filename used for a result file
@@ -149,19 +151,20 @@ def get_weights_for_reweighting(
starting_index = 0
- dict_samples = [{key: sample[key] for key in result.posterior} for i,sample in result.posterior.iterrows()]
+ dict_samples = [{key: sample[key] for key in result.posterior}
+ for _, sample in result.posterior.iterrows()]
n = len(dict_samples) - starting_index
# Helper function to compute likelihoods in parallel
def eval_pool(this_logl):
with multiprocessing.Pool(processes=npool) as pool:
- chunksize = max(100,n//(2*npool))
+ chunksize = max(100, n // (2 * npool))
return list(tqdm(
- pool.imap(partial(__eval_l,this_logl),
- dict_samples[starting_index:], chunksize=chunksize),
- desc = 'Computing likelihoods',
- total = n
- ))
+ pool.imap(partial(__eval_l, this_logl),
+ dict_samples[starting_index:], chunksize=chunksize),
+ desc='Computing likelihoods',
+ total=n)
+ )
if old_likelihood is None:
old_log_likelihood_array[starting_index:] = \
@@ -177,9 +180,9 @@ def get_weights_for_reweighting(
# Compute priors
for ii, sample in enumerate(tqdm(dict_samples[starting_index:],
- desc = 'Computing priors',
- total = n),
- start = starting_index):
+ desc='Computing priors',
+ total=n),
+ start=starting_index):
if old_prior is not None:
old_log_prior_array[ii] = old_prior.ln_prob(dict_samples[ii])
else:
--
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