From b9bb2f47612b874de6387cbed11c75c30645c700 Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Tue, 27 Sep 2016 12:14:40 +0200
Subject: [PATCH] Fixes summary
1) Improves the output
2) Fixes a bug in which the while loop got stcuk attempting to rename
the glitch parameters (when ng > 1)
---
pyfstat.py | 52 ++++++++++++++++++++++++++++++++--------------------
1 file changed, 32 insertions(+), 20 deletions(-)
diff --git a/pyfstat.py b/pyfstat.py
index b76daf3..6442f3a 100755
--- a/pyfstat.py
+++ b/pyfstat.py
@@ -995,31 +995,39 @@ class MCMCSearch(BaseSearchClass):
maxtwoF = self.lnlikes[jmax]
d = OrderedDict()
- lnl_finite = copy.copy(self.lnlikes)
- lnl_finite[idxs] = 0
- close_idxs = abs((maxtwoF - lnl_finite) / maxtwoF) < threshold
+ repeats = []
for i, k in enumerate(self.theta_keys):
- ng = 1
- while k in d:
- if k == 1:
- k = k + '_1'
- else:
- k.replace('_{}'.format(ng-1), '_{}'.format(ng))
- ng += 1
+ if k in d and k not in repeats:
+ d[k+'_0'] = d[k] # relabel the old key
+ d.pop(k)
+ repeats.append(k)
+ if k in repeats:
+ k = k + '_0'
+ count = 1
+ while k in d:
+ k = k.replace('_{}'.format(count-1), '_{}'.format(count))
+ count += 1
d[k] = self.samples[jmax][i]
-
- s = self.samples[:, i][close_idxs]
- d[k + '_std'] = np.std(s)
return d, maxtwoF
def get_median_stds(self):
""" Returns a dict of the median and std of all production samples """
d = OrderedDict()
+ repeats = []
for s, k in zip(self.samples.T, self.theta_keys):
- ng = 1
- while k in d:
- k = k.rstrip('_{}'.format(ng-1)) + '_{}'.format(ng)
- ng += 1
+ if k in d and k not in repeats:
+ d[k+'_0'] = d[k] # relabel the old key
+ d[k+'_0_std'] = d[k+'_std']
+ d.pop(k)
+ d.pop(k+'_std')
+ repeats.append(k)
+ if k in repeats:
+ k = k + '_0'
+ count = 1
+ while k in d:
+ k = k.replace('_{}'.format(count-1), '_{}'.format(count))
+ count += 1
+
d[k] = np.median(s)
d[k+'_std'] = np.std(s)
return d
@@ -1044,13 +1052,17 @@ class MCMCSearch(BaseSearchClass):
f.write('{} = {:1.16e}\n'.format(key, val))
def print_summary(self):
- d, max_twoF = self.get_max_twoF()
+ max_twoFd, max_twoF = self.get_max_twoF()
median_std_d = self.get_median_stds()
- print('Max twoF: {}'.format(max_twoF))
+ print('\nSummary:')
print('theta0 index: {}'.format(self.theta0_idx))
+ print('Max twoF: {} with parameters:'.format(max_twoF))
+ for k in np.sort(max_twoFd.keys()):
+ print(' {:10s} = {:1.9e}'.format(k, max_twoFd[k]))
+ print('\nMedian +/- std for production values')
for k in np.sort(median_std_d.keys()):
if 'std' not in k:
- print('{:10s} = {:1.9e} +/- {:1.9e}'.format(
+ print(' {:10s} = {:1.9e} +/- {:1.9e}'.format(
k, median_std_d[k], median_std_d[k+'_std']))
--
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