From b1cf1ff5960259af030d42bb563a45ac5c59d568 Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Wed, 16 May 2018 08:12:03 +1000
Subject: [PATCH] Replace double underscores
---
tupak/sampler.py | 28 ++++++++++++++--------------
1 file changed, 14 insertions(+), 14 deletions(-)
diff --git a/tupak/sampler.py b/tupak/sampler.py
index 1f53b5ace..d2041934b 100644
--- a/tupak/sampler.py
+++ b/tupak/sampler.py
@@ -46,11 +46,11 @@ class Sampler(object):
self.use_ratio = use_ratio
self.external_sampler = external_sampler
- self.search_parameter_keys = []
- self.fixed_parameter_keys = []
+ self.__search_parameter_keys = []
+ self.__fixed_parameter_keys = []
self.initialise_parameters()
self.verify_parameters()
- self.ndim = len(self.search_parameter_keys)
+ self.ndim = len(self.__search_parameter_keys)
self.kwargs = kwargs
self.result = result
@@ -69,10 +69,10 @@ class Sampler(object):
def result(self, result):
if result is None:
self.__result = Result()
- self.__result.search_parameter_keys = self.search_parameter_keys
+ self.__result.__search_parameter_keys = self.__search_parameter_keys
self.__result.parameter_labels = [
self.priors[k].latex_label for k in
- self.search_parameter_keys]
+ self.__search_parameter_keys]
self.__result.label = self.label
self.__result.outdir = self.outdir
elif type(result) is Result:
@@ -123,17 +123,17 @@ class Sampler(object):
for key in self.priors:
if isinstance(self.priors[key], Prior) is True \
and self.priors[key].is_fixed is False:
- self.search_parameter_keys.append(key)
+ self.__search_parameter_keys.append(key)
elif isinstance(self.priors[key], Prior) \
and self.priors[key].is_fixed is True:
self.likelihood.parameters[key] = \
self.priors[key].sample()
- self.fixed_parameter_keys.append(key)
+ self.__fixed_parameter_keys.append(key)
logging.info("Search parameters:")
- for key in self.search_parameter_keys:
+ for key in self.__search_parameter_keys:
logging.info(' {} ~ {}'.format(key, self.priors[key]))
- for key in self.fixed_parameter_keys:
+ for key in self.__fixed_parameter_keys:
logging.info(' {} = {}'.format(key, self.priors[key].peak))
def verify_parameters(self):
@@ -144,15 +144,15 @@ class Sampler(object):
"Source model does not contain keys {}".format(unmatched_keys))
def prior_transform(self, theta):
- return [self.priors[key].rescale(t) for key, t in zip(self.search_parameter_keys, theta)]
+ return [self.priors[key].rescale(t) for key, t in zip(self.__search_parameter_keys, theta)]
def log_prior(self, theta):
return np.sum(
[np.log(self.priors[key].prob(t)) for key, t in
- zip(self.search_parameter_keys, theta)])
+ zip(self.__search_parameter_keys, theta)])
def log_likelihood(self, theta):
- for i, k in enumerate(self.search_parameter_keys):
+ for i, k in enumerate(self.__search_parameter_keys):
self.likelihood.parameters[k] = theta[i]
if self.use_ratio:
return self.likelihood.log_likelihood_ratio()
@@ -170,7 +170,7 @@ class Sampler(object):
"""
draw = np.array([self.priors[key].sample()
- for key in self.search_parameter_keys])
+ for key in self.__search_parameter_keys])
if np.isinf(self.log_likelihood(draw)):
logging.info('Prior draw {} has inf likelihood'.format(draw))
if np.isinf(self.log_prior(draw)):
@@ -433,7 +433,7 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
else:
result.log_bayes_factor = result.logz - result.noise_logz
result.injection_parameters = injection_parameters
- result.fixed_parameter_keys = [key for key in priors if isinstance(key, prior.DeltaFunction)]
+ result.__fixed_parameter_keys = [key for key in priors if isinstance(key, prior.DeltaFunction)]
result.priors = priors
result.kwargs = sampler.kwargs
result.samples_to_data_frame()
--
GitLab