From 157060f78b561500ae09ebf21c4dfd6426cc4acd Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Tue, 11 Oct 2016 16:43:45 +0200
Subject: [PATCH] Adds printing of twoF value when using BGSL
Also fixes big in generate_initial_p0
---
pyfstat.py | 27 +++++++++++++++++++++------
1 file changed, 21 insertions(+), 6 deletions(-)
diff --git a/pyfstat.py b/pyfstat.py
index 7e92fd2..e047aa3 100755
--- a/pyfstat.py
+++ b/pyfstat.py
@@ -952,7 +952,7 @@ class MCMCSearch(BaseSearchClass):
if type(self.theta_initial) == dict:
logging.info('Generate initial values from initial dictionary')
- if self.nglitch > 1:
+ if hasattr(self, 'nglitch') and self.nglitch > 1:
raise ValueError('Initial dict not implemented for nglitch>1')
p0 = [[[self.generate_rv(**self.theta_initial[key])
for key in self.theta_keys]
@@ -1006,12 +1006,17 @@ class MCMCSearch(BaseSearchClass):
lnp_finite = copy.copy(lnp)
lnp_finite[np.isinf(lnp)] = np.nan
idx = np.unravel_index(np.nanargmax(lnp_finite), lnp_finite.shape)
- logging.info(('Gen. new p0 from max lnp (walker {}, pos {})'
- ' which had twoF={} ')
- .format(idx[0], idx[1], lnl[idx]))
p = pF[idx]
p0 = self.generate_scattered_p0(p)
+ self.search.BSGL = False
+ twoF = self.logl(p, self.search)
+ self.search.BSGL = self.BSGL
+
+ logging.info(('Gen. new p0 from pos {} which had det. stat.={:2.1f},'
+ ' twoF={:2.1f} and lnp={:2.1f}')
+ .format(idx[1], lnl[idx], twoF, lnp_finite[idx]))
+
return p0
def get_save_data_dictionary(self):
@@ -1096,7 +1101,7 @@ class MCMCSearch(BaseSearchClass):
return False
def get_max_twoF(self, threshold=0.05):
- """ Returns the max 2F sample and the corresponding 2F value
+ """ Returns the max likelihood sample and the corresponding 2F value
Note: the sample is returned as a dictionary along with an estimate of
the standard deviation calculated from the std of all samples with a
@@ -1111,9 +1116,19 @@ class MCMCSearch(BaseSearchClass):
logging.info('twoF values contain nan')
idxs = np.isfinite(self.lnlikes)
jmax = np.nanargmax(self.lnlikes[idxs])
- maxtwoF = self.lnlikes[jmax]
+ maxlogl = self.lnlikes[jmax]
d = OrderedDict()
+ if self.BSGL:
+ if hasattr(self, 'search') is False:
+ self.inititate_search_object()
+ p = self.samples[jmax]
+ self.search.BSGL = False
+ maxtwoF = self.logl(p, self.search)
+ self.search.BSGL = self.BSGL
+ else:
+ maxtwoF = maxlogl
+
repeats = []
for i, k in enumerate(self.theta_keys):
if k in d and k not in repeats:
--
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