u = np.cos(inc)
u2 = np.square(u)
# Signal models for each detector.
H = filter.sngl_inspiral_psd(waveform,
mass1=sim_inspiral.mass1,
mass2=sim_inspiral.mass2,
spin1x=sim_inspiral.spin1x,
spin1y=sim_inspiral.spin1y,
spin1z=sim_inspiral.spin1z,
spin2x=sim_inspiral.spin2x,
spin2y=sim_inspiral.spin2y,
spin2z=sim_inspiral.spin2z,
f_min=f_low)
W = [filter.signal_psd_series(H, psds[ifo]) for ifo in opts.detector]
signal_models = [timing.SignalModel(_) for _ in W]
# Get SNR=1 horizon distances for each detector.
horizons = np.asarray([
signal_model.get_horizon_distance() for signal_model in signal_models
])
# Get antenna factors for each detector.
Fplus, Fcross = np.asarray([
lal.ComputeDetAMResponse(response, ra, dec, psi, gmst)
for response in responses
]).T
# Compute TOAs at each detector.
toas = np.asarray([
lal.TimeDelayFromEarthCenter(location, ra, dec, epoch)
phi = sim_inspiral.coa_phase
psi = sim_inspiral.polarization
epoch = lal.LIGOTimeGPS(
sim_inspiral.geocent_end_time, sim_inspiral.geocent_end_time_ns)
gmst = lal.GreenwichMeanSiderealTime(epoch)
waveform = sim_inspiral.waveform if opts.waveform is None else opts.waveform
approximant, amplitude_order, phase_order = \
timing.get_approximant_and_orders_from_string(waveform)
# Pre-evaluate some trigonometric functions that we will need.
u = np.cos(inc)
u2 = np.square(u)
# Signal models for each detector.
signal_models = [
timing.SignalModel(m1, m2, psds[ifo], f_low,
approximant, amplitude_order, phase_order)
for ifo in opts.detector]
# Get SNR=1 horizon distances for each detector.
horizons = np.asarray([signal_model.get_horizon_distance()
for signal_model in signal_models])
# Get antenna factors for each detector.
Fplus, Fcross = np.asarray([
lal.ComputeDetAMResponse(response, ra, dec, psi, gmst)
for response in responses]).T
# Compute TOAs at each detector.
toas = np.asarray([lal.TimeDelayFromEarthCenter(location, ra, dec,
epoch) for location in locations])
_f = np.atleast_1d(f)
ret = np.empty(_f.shape, dtype=float)
cond = (5 <= _f) & (_f <= 1800)
ret[cond] = self.func(_f[cond])
ret[~cond] = 0.
if np.isscalar(f):
ret = np.asscalar(ret)
return ret
psdfuncs = [psdfunc(psds[ifo]) for ifo in opts.detector]
# Compute horizon distances for each template, for each detector
progress.update(-1, 'computing signal models')
horizons_bank = [
[
timing.SignalModel(sngl.mass1, sngl.mass2, S, template_bank_f_low, template_approximant, template_amplitude_order, template_phase_order).get_horizon_distance()
for S in psdfuncs
] for sngl in template_bank]
# Generate templates for each unique set of intrinsic parameters
# FIXME: Get template_duration, template_approximant, template_amplitude_order, and template_phase_order from sngl_inspiral table.
progress.update(-1, 'computing template bank')
template_bank = [
[
filter.generate_template(sngl.mass1, sngl.mass2, S, template_bank_f_low, sample_rate, template_duration, template_approximant, template_amplitude_order, template_phase_order)
for S in psdfuncs
] for sngl in template_bank]
# Generate PSDs for data coloring
progress.update(-1, 'computing PSDs')
def generate_psd(S):
from lalinference.bayestar import filter
from lalinference.bayestar import timing
def abs2(x):
return np.square(np.real(x)) + np.square(np.imag(x))
mass1 = 1.4
mass2 = 1.4
f_low = 40
S = timing.get_noise_psd_func('H1')
# Compute Fisher matrix elements for full signal (for comparison).
signal_model = timing.SignalModel(
mass1, mass2, S, f_low,
*timing.get_approximant_and_orders_from_string('TaylorF2threePointFivePN'))
w1 = signal_model.get_sn_moment(1)
w2 = signal_model.get_sn_moment(2)
# Compute 1 second of autocorrelation sequence.
# FIXME: make this the longest lag that we are going to integrate over.
acor_series, sample_rate = filter.autocorrelation(
mass1, mass2, S, f_low, 1,
*timing.get_approximant_and_orders_from_string('TaylorF2threePointFivePN'))
# Augment autocorrelation sequence with negative time lags.
n = len(acor_series)
t = np.arange(1 - n, n) / sample_rate
acor_series = np.concatenate((np.conj(acor_series[:0:-1]), acor_series))