def get_livetimes(self, fi):
with h5py.File(fi, 'r') as f:
for ct in self.ctimes:
# 'inclusive' time when at least the ifos specified by ct are on
fgt = conv.sec_to_year(f[ct].attrs['foreground_time'])
# index dict on chunk start time / coinc type
self.incl_livetimes[(get_start_dur(fi)[0], ct)] = fgt
# subtract times during which 1 more ifo was on,
# ie subtract H1L1* time from H1L1; subtract H1* time from H1; etc
for combo in self.moreifotimes(ct):
if len(combo) == len(ct) + 2:
fgt -= conv.sec_to_year(
f[combo].attrs['foreground_time'])
# index dict on chunk start time / coinc time
self.livetimes[(get_start_dur(fi)[0], ct)] = fgt
def calculate_ifar(self, newsnr, duration):
if self.fit_info['fixed_ifar']:
return self.fit_info['fixed_ifar']
dur_bin = self.fit_info['bins'][duration]
rate = self.fit_info['rates'][dur_bin]
coeff = self.fit_info['coeffs'][dur_bin]
rate_louder = rate * fits.cum_fit('exponential', [newsnr], coeff,
self.fit_info['thresh'])[0]
# apply a trials factor of the number of duration bins
rate_louder *= len(self.fit_info['rates'])
return conv.sec_to_year(1. / rate_louder)
def add_background(self, full_file):
start = get_start_dur(full_file)[0]
self.get_livetimes(full_file)
with h5py.File(full_file, 'r') as ff:
# get stat values and threshold
_bgstat = ff['background_exc/stat'][:]
_keepstat = _bgstat > self.thr
# get template ids and filter
_bgtid = ff['background_exc/template_id'][:]
# need the template filter to have already been made
assert self.in_bin is not None
_keep = np.logical_and(_keepstat, self.in_bin[_bgtid])
_bgstat = _bgstat[_keep]
_bgdec = ff['background_exc/decimation_factor'][:][_keep]
# assign coinc types
_times = {}
for i in self.ifos:
# NB times are time-shifted between ifos
_times[i] = ff['background_exc/' + i + '/time'][:][_keep]
_ctype = self.get_ctypes(_times)
for cty in self.coinc_types:
self.bg_vals[(start, cty)] = _bgstat[_ctype == cty]
self.bg_dec[(start, cty)] = _bgdec[_ctype == cty]
# get bg livetime for noise rate estimate
# - convert to years
self.bg_livetimes[(start, cty)] = conv.sec_to_year(
ff[cty].attrs['background_time_exc'])
# make histogram
bins = self.make_bins(np.max(_bgstat[_ctype == cty]), 'bg')
# hack to make larger bins for H1L1V1
if cty == 'H1L1V1':
if self.args.verbose:
print('Halving bg bins for triple bg hist')
bins = bins[::2].copy() # take every 2nd bin edge
self.bg_hist[(start, cty)] = \
np.histogram(_bgstat[_ctype == cty],
weights=_bgdec[_ctype == cty], bins=bins)
# get expected number of bg events for this chunk and coinc type
self.exp_bg[(start, cty)] = _bgdec[_ctype == cty].sum() * \
self.incl_livetimes[(start, cty)] / \
self.bg_livetimes[(start, cty)]
def calculate_ifar(self, sngl_ranking, duration):
if self.fixed_ifar:
return self.fixed_ifar[self.ifo]
with h5py.File(self.fit_file, 'r') as fit_file:
bin_edges = fit_file['bins_edges'][:]
live_time = fit_file[self.ifo].attrs['live_time']
thresh = fit_file.attrs['fit_threshold']
dist_grp = fit_file[self.ifo][self.sngl_ifar_est_dist]
rates = dist_grp['counts'][:] / live_time
coeffs = dist_grp['fit_coeff'][:]
bins = bin_utils.IrregularBins(bin_edges)
dur_bin = bins[duration]
rate = rates[dur_bin]
coeff = coeffs[dur_bin]
rate_louder = rate * fits.cum_fit('exponential', [sngl_ranking], coeff,
thresh)[0]
# apply a trials factor of the number of duration bins
rate_louder *= len(rates)
return conv.sec_to_year(1. / rate_louder)