def filter_templates(self):
"""
calculate array of Booleans in template id order to filter events
"""
assert self.massspins is not None
assert self.lo is not None
assert self.hi is not None
if self.args.verbose:
print('Cutting on %s between %f - %f' %
(self.bin_param, self.lo, self.hi))
self.tpars = triggers.get_param(self.bin_param, None, *self.massspins)
self.in_bin = filter_bin_lo_hi(self.tpars, self.lo, self.hi)
def template_param_bin_calc(padata, trdata, horizons):
"""
Parameters
----------
padata: PAstroData instance
Static information on p astro calculation
trdata: dictionary
Trigger properties
horizons: dictionary
BNS horizon distances keyed on ifo
Returns
-------
p_astro, p_terr: tuple of floats
"""
massspin = (trdata['mass1'], trdata['mass2'],
trdata['spin1z'], trdata['spin2z'])
trig_param = triggers.get_param(padata.spec['param'], None, *massspin)
# NB digitize gives '1' for first bin, '2' for second etc.
bind = numpy.digitize(trig_param, padata.bank['bin_edges']) - 1
logging.debug('Trigger %s is in bin %i', padata.spec['param'], bind)
# Get noise rate density
if 'bg_fac' not in padata.spec:
expfac = 6.
else:
expfac = padata.spec['bg_fac']
# FAR is in Hz, therefore convert to rate per year (per SNR)
dnoise = noise_density_from_far(trdata['far'], expfac) * lal_s_per_yr
logging.debug('FAR %.3g, noise density per yr per SNR %.3g',
trdata['far'], dnoise)
# Scale by fraction of templates in bin
dnoise *= padata.bank['tcounts'][bind] / padata.bank['num_t']
logging.debug('Noise density in bin %.3g', dnoise)
# Get signal rate density at given SNR
dsig = signal_pdf_from_snr(trdata['network_snr'],
padata.spec['netsnr_thresh'])
logging.debug('SNR %.3g, signal pdf %.3g', trdata['network_snr'], dsig)
dsig *= padata.spec['sig_per_yr_binned'][bind]
logging.debug('Signal density per yr per SNR in bin %.3g', dsig)
# Scale by network sensitivity accounting for BNS horizon distances
dsig *= signal_rate_rescale(horizons, padata.spec['ref_bns_horizon'])
logging.debug('After horizon rescaling %.3g', dsig)
p_astro = dsig / (dsig + dnoise)
logging.debug('p_astro %.4g', p_astro)
return p_astro, 1 - p_astro