reference_psd_filenames_by_process_id = ligolw_bayestar.psd_filenames_by_process_id_for_xmldoc(xmldoc)
@memoized
def reference_psds_for_filename(filename):
xmldoc = ligolw_utils.load_filename(
filename, contenthandler=lal.series.PSDContentHandler)
psds = lal.series.read_psd_xmldoc(xmldoc)
return dict(
(key, timing.InterpolatedPSD(filter.abscissa(psd), psd.data.data))
for key, psd in psds.iteritems() if psd is not None)
def reference_psd_for_ifo_and_filename(ifo, filename):
return reference_psds_for_filename(filename)[ifo]
f_low = opts.f_low
approximant, amplitude_order, phase_order = timing.get_approximant_and_orders_from_string(opts.waveform)
count_sky_maps_failed = 0
# Loop over all coinc_event <-> sim_inspiral coincs.
for coinc, sngl_inspirals in ligolw_bayestar.coinc_and_sngl_inspirals_for_xmldoc(xmldoc):
instruments = set(sngl_inspiral.ifo for sngl_inspiral in sngl_inspirals)
# Look up PSDs
log.info('%s:reading PSDs', coinc.coinc_event_id)
psds = tuple(
reference_psd_for_ifo_and_filename(sngl_inspiral.ifo,
reference_psd_filenames_by_process_id[sngl_inspiral.process_id])
for sngl_inspiral in sngl_inspirals)
# Unpack some values from the row in the table.
m1 = sim_inspiral.mass1
m2 = sim_inspiral.mass2
f_low = sim_inspiral.f_lower if opts.f_low is None else opts.f_low
DL = sim_inspiral.distance
ra = sim_inspiral.longitude
dec = sim_inspiral.latitude
inc = sim_inspiral.inclination
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])
# Unpack some values from the row in the table.
m1 = sim_inspiral.mass1
m2 = sim_inspiral.mass2
f_low = sim_inspiral.f_lower if opts.f_low is None else opts.f_low
DL = sim_inspiral.distance
ra = sim_inspiral.longitude
dec = sim_inspiral.latitude
inc = sim_inspiral.inclination
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])
import scipy.special
import itertools
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))
# Construct spline interpolant and derivatives for real part.
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))
# Construct spline interpolant and derivatives for real part.
