Commit 300ad290 authored by Gregory Ashton's avatar Gregory Ashton
Browse files

Complete run of MC results

Use N=1000 for the recoveries and N=10000 for the test in noise
parent 435e631e
Paper/allsky_recovery.png

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Paper/allsky_recovery.png

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Paper/allsky_recovery.png
Paper/allsky_recovery.png
Paper/allsky_recovery.png
Paper/allsky_recovery.png
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Paper/directed_recovery.png

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Paper/directed_recovery.png

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Paper/directed_recovery.png
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......@@ -52,6 +52,8 @@ else:
parser = argparse.ArgumentParser()
parser.add_argument("-q", "--quite", help="Decrease output verbosity",
action="store_true")
parser.add_argument("--no-interactive", help="Don't use interactive output",
action="store_true")
parser.add_argument("-c", "--clean", help="Don't use cached data",
action="store_true")
parser.add_argument("-u", "--use-old-data", action="store_true")
......@@ -61,7 +63,7 @@ parser.add_argument('unittest_args', nargs='*')
args, unknown = parser.parse_known_args()
sys.argv[1:] = args.unittest_args
if args.quite:
if args.quite or args.no_interactive:
def tqdm(x, *args, **kwargs):
return x
......@@ -431,7 +433,7 @@ class ComputeFstat(object):
detector_names = list(set([d.header.name for d in SFTCatalog.data]))
self.detector_names = detector_names
SFT_timestamps = [d.header.epoch for d in SFTCatalog.data]
if args.quite is False:
if args.quite is False and args.no_interactive is False:
try:
from bashplotlib.histogram import plot_hist
print('Data timestamps histogram:')
......@@ -1749,17 +1751,18 @@ class MCMCSearch(BaseSearchClass):
def print_summary(self):
max_twoFd, max_twoF = self.get_max_twoF()
median_std_d = self.get_median_stds()
print('\nSummary:')
logging.info('Summary:')
if hasattr(self, 'theta0_idx'):
print('theta0 index: {}'.format(self.theta0_idx))
print('Max twoF: {} with parameters:'.format(max_twoF))
logging.info('theta0 index: {}'.format(self.theta0_idx))
logging.info('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')
logging.info('Median +/- 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(
logging.info(' {:10s} = {:1.9e} +/- {:1.9e}'.format(
k, median_std_d[k], median_std_d[k+'_std']))
logging.info('\n')
def CF_twoFmax(self, theta, twoFmax, ntrials):
Fmax = twoFmax/2.0
......
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