def finalize_as_template(self):
if not self.bank.use_metric:
return
df, PSD = get_neighborhood_PSD([self], self.flow,
self.bank.noise_model)
curr_len = (len(PSD) - self.flow // df)
length_check = curr_len > self.bank._moments[df][0].length
if df not in self.bank._moments or length_check:
real8vector_psd = CreateREAL8Vector(len(PSD))
real8vector_psd.data[:] = PSD
self.bank._moments[df] = create_moments(df, self.flow, len(PSD))
lalsim.SimInspiralTaylorF2RedSpinComputeNoiseMoments(
*(self.bank._moments[df] + (real8vector_psd, self.flow, df))
)
self._metric = lalsim.SimInspiralTaylorF2RedSpinMetricChirpTimes(
self._theta0,
self._theta3,
self._theta3s,
self.flow,
df,
*self.bank._moments[df]
)
if isnan(self._metric[0]):
raise ValueError("g00 is nan")
# Transform to chirp times.
thetas_0 = lalsimulation.SimInspiralTaylorF2RedSpinChirpTimesFromMchirpEtaChi(
mchirp0, eta0, chi0, f_low)
# Sampled PSD.
f_high = 2048
df = 0.1
S = lal.CreateREAL8Vector(int(f_high // df))
S.data = [lalsimulation.SimNoisePSDaLIGOZeroDetHighPower(i * df)
for i in range(len(S.data))]
# Allocate noise moments.
moments = [lal.CreateREAL8Vector(int((f_high - f_low) // df)) for _ in range(29)]
# Compute noise moments.
lalsimulation.SimInspiralTaylorF2RedSpinComputeNoiseMoments(
*(moments + [S, f_low, df]))
# Compute Fisher information matrix. Note factor of 2:
# The function SimInspiralTaylorF2RedSpinFisherMatrixChirpTimes
# returns the Fisher matrix for an SNR of 1/sqrt(2). The factor
# of 2 makes this the Fisher matrix at unit SNR.
I = lalsimulation.SimInspiralTaylorF2RedSpinFisherMatrixChirpTimes(
*(thetas_0 + [f_low, df] + moments)).data * 2
# Now amplify to the requested SNR.
I *= np.square(opts.snr)
# Blockwise separation of Fisher matrix. Parameters are in the following order:
# theta0, theta3, theta3S, t0, phi0
IA = I[0:3, 0:3] # intrinsic block
IB = I[0:3, 3:5] # cross block
