def sigma_cached(self, psd):
""" Cache sigma calculate for use in tandem with the FilterBank class
"""
key = id(psd)
if not hasattr(psd, '_sigma_cached_key'):
psd._sigma_cached_key = {}
if key not in self._sigmasq or id(self) not in psd._sigma_cached_key:
psd._sigma_cached_key[id(self)] = True
# If possible, we precalculate the sigmasq vector for all possible waveforms
if pycbc.waveform.waveform_norm_exists(self.approximant):
if not hasattr(psd, 'sigmasq_vec'):
psd.sigmasq_vec = {}
if self.approximant not in psd.sigmasq_vec:
psd.sigmasq_vec[
self.
approximant] = pycbc.waveform.get_waveform_filter_norm(
self.approximant, psd, len(psd), psd.delta_f,
self.f_lower)
if not hasattr(self, 'sigma_scale'):
# Get an amplitude normalization (mass dependant constant norm)
amp_norm = pycbc.waveform.get_template_amplitude_norm(
self.params, approximant=self.approximant)
amp_norm = 1 if amp_norm is None else amp_norm
self.sigma_scale = (DYN_RANGE_FAC * amp_norm)**2.0
self._sigmasq[key] = self.sigma_scale * \
psd.sigmasq_vec[self.approximant][self.end_idx-1]
else:
if not hasattr(self, 'sigma_view'):
from pycbc.filter.matchedfilter import get_cutoff_indices
N = (len(self) - 1) * 2
kmin, kmax = get_cutoff_indices(
self.min_f_lower or self.f_lower, self.end_frequency,
self.delta_f, N)
self.sslice = slice(kmin, kmax)
self.sigma_view = self[
self.sslice].squared_norm() * 4.0 * self.delta_f
if not hasattr(psd, 'invsqrt'):
psd.invsqrt = 1.0 / psd[self.sslice]
self._sigmasq[key] = self.sigma_view.inner(psd.invsqrt)
return self._sigmasq[key]
def sigma_cached(self, psd):
""" Cache sigma calculate for use in tandem with the FilterBank class
"""
key = id(psd)
if not hasattr(psd, '_sigma_cached_key'):
psd._sigma_cached_key = {}
if key not in self._sigmasq or id(self) not in psd._sigma_cached_key:
psd._sigma_cached_key[id(self)] = True
# If possible, we precalculate the sigmasq vector for all possible waveforms
if pycbc.waveform.waveform_norm_exists(self.approximant):
if not hasattr(psd, 'sigmasq_vec'):
psd.sigmasq_vec = {}
if self.approximant not in psd.sigmasq_vec:
psd.sigmasq_vec[self.approximant] = pycbc.waveform.get_waveform_filter_norm(
self.approximant, psd, len(psd), psd.delta_f, self.f_lower)
if not hasattr(self, 'sigma_scale'):
# Get an amplitude normalization (mass dependant constant norm)
amp_norm = pycbc.waveform.get_template_amplitude_norm(
self.params, approximant=self.approximant)
amp_norm = 1 if amp_norm is None else amp_norm
self.sigma_scale = (DYN_RANGE_FAC * amp_norm) ** 2.0
self._sigmasq[key] = self.sigma_scale * \
psd.sigmasq_vec[self.approximant][self.end_idx-1]
else:
if not hasattr(self, 'sigma_view'):
from pycbc.filter.matchedfilter import get_cutoff_indices
N = (len(self) -1) * 2
kmin, kmax = get_cutoff_indices(
self.min_f_lower or self.f_lower, self.end_frequency,
self.delta_f, N)
self.sslice = slice(kmin, kmax)
self.sigma_view = self[self.sslice].squared_norm() * 4.0 * self.delta_f
if not hasattr(psd, 'invsqrt'):
psd.invsqrt = 1.0 / psd[self.sslice]
self._sigmasq[key] = self.sigma_view.inner(psd.invsqrt)
return self._sigmasq[key]
