det = str(sngl_row.ifo)
print(det, rhoExpected[det])
m1 = m1 * lal.LAL_MSUN_SI
m2 = m2 * lal.LAL_MSUN_SI
rho2Net = 0
for det in rhoExpected:
rho2Net += rhoExpected[det]**2
if rosDebugMessagesDictionary["DebugMessages"]:
print(" Network :", np.sqrt(rho2Net))
# Create a 'best recovered signal'
Psig = lalsimutils.ChooseWaveformParams(
m1=m1,
m2=m2,
approx=approxSignal,
fmin=fminWavesSignal,
dist=factored_likelihood.distMpcRef * 1e6 *
lal.LAL_PC_SI, # default distance
fref=fref,
tref=event_time_gps,
ampO=ampO
) # FIXME: Parameter mapping from trigger space to search space
if rosDebugMessagesDictionary["DebugMessages"]:
print(
" === Coinc table : estimated signal [overridden if injection] ==="
)
Psig.print_params()
# Read in *injection* XML
if opts.inj:
print("Loading injection XML:", opts.inj)
Psig = lalsimutils.xml_to_ChooseWaveformParams_array(str(opts.inj))[
sngl_insp_table.sync_next_id()
for (m1, m2) in itertools.chain(*grid):
sngl_insp = sngl_insp_table.RowType()
sngl_insp.event_id = sngl_insp_table.get_next_id()
#sngl_insp.mass1, sngl_insp.mass2 = m1/lal.LAL_MSUN_SI, m2/lal.LAL_MSUN_SI
sngl_insp.mass1, sngl_insp.mass2 = m1, m2
sngl_insp.process_id = proc_id
sngl_insp_table.append(sngl_insp)
return sngl_insp_table
# Setup signal and IP class
m1 = 1.5 * lal.LAL_MSUN_SI
m2 = 1.35 * lal.LAL_MSUN_SI
PSIG = lsu.ChooseWaveformParams(m1=m1, m2=m2, approx=lalsim.TaylorT1)
PTEST = PSIG.copy() # find deltaF for lower end of range we're looking in
PTEST.m1 *= 0.9
PTEST.m2 *= 0.9
PSIG.deltaF = lsu.findDeltaF(PTEST)
IP = lsu.Overlap(fLow=40., deltaF=PSIG.deltaF, psd=lal.LIGOIPsd)
PTMPLT = PSIG.copy()
hfSIG = lsu.norm_hoff(PSIG, IP)
McSIG = lsu.mchirp(m1, m2)
etaSIG = lsu.symRatio(m1, m2)
NMcs = 11
NEtas = 11
param_names = ['Mc', 'eta']
# Find appropriate parameter ranges
param_ranges = eff.find_effective_Fisher_region(
# Generate an inner product using the original discrete PSD
# Perform an inner product using it *and* using an analytic PSD
#
# Populate signal
m1 = 10*lal.LAL_MSUN_SI
m2 = 10*lal.LAL_MSUN_SI
df = psd_dict[detectors[0]].deltaF
fSample = df * 2 *( len(psd_dict[detectors[0]].data.data)-1) # rescale
print("To construct a signal, we reconstruct the sampling rate and time window, consistent with the default PSD sampling: (fSample, 1/df) = ", fSample, 1/df)
Psig = lalsimutils.ChooseWaveformParams(
m1 = m1,m2 =m2,
fmin = 30,
fref=100, ampO=0,
tref = lal.GPSTimeNow(), # factored_likelihood requires GPS be assigned
radec=True, theta=1.2, phi=2.4,
detector='H1',
dist=25.*1.e6*lal.LAL_PC_SI,
deltaT=1./fSample,
deltaF = df
)
data_dict={}
data_dict['H1'] = lalsimutils.non_herm_hoff(Psig)
Psig.detector = 'L1'
data_dict['L1'] = lalsimutils.non_herm_hoff(Psig)
Psig.detector = 'V1'
data_dict['V1'] = lalsimutils.non_herm_hoff(Psig)
psd_analytic_dict = {}
psd_analytic_dict['H1'] = lalsim.SimNoisePSDaLIGOZeroDetHighPower# lal.LIGOIPsd
psd_analytic_dict['L1'] = lalsim.SimNoisePSDaLIGOZeroDetHighPower #lal.LIGOIPsd
psd_analytic_dict['V1'] = lalsim.SimNoisePSDaLIGOZeroDetHighPower # lal.LIGOIPsd
print(" ======= Template specified: precomputing all quantities ==========")
# Struct to hold template parameters
# Fiducial distance provided but will not be used
m1 = 4 * lal.MSUN_SI
m2 = 4 * lal.MSUN_SI
ampO = opts.amporder # sets which modes to include in the physical signal
Lmax = opts.Lmax # sets which modes to include
fref = opts.fref
P = lalsimutils.ChooseWaveformParams(
fmin=fminWaves,
radec=False,
m1=m1,
m2=m2,
ampO=ampO,
approx=approxTemplate,
fref=fref,
deltaT=1. / fSample,
tref=theEpochFiducial,
dist=100 * 1.e6 *
lal.PC_SI, # critical that this is fiducial (will be reset later)
deltaF=df)
tStartPrecompute = lal.GPSTimeNow()
rholms_intp, crossTerms, crossTermsV, rholms, rest = factored_likelihood.PrecomputeLikelihoodTerms(
theEpochFiducial,
0.1,
P,
data_dict,
psd_dict,
Lmax,
2000,
# Create artificial "signal". Needed to minimize duplicate code when I
# - consistently test waveform duration
# - copy template parameters
# - [test code] : generate plots vs time [test code], expected SNR, etc
# WARNING: Test code plots will not look perfect, because we don't know the true sky location (or phase, polarization, ...)
if not Psig and opts.channel_name: # If data loaded but no signal generated
if (not opts.template_mass1) or (not opts.template_mass2) or (
not opts.force_gps_time):
print(
" CANCEL: For frame-file reading, arguments --mass1 --mass2 --event-time all required "
)
# print opts.template_mass1, opts.template_mass2,opts.force_gps_time
sys.exit(0)
Psig = lalsimutils.ChooseWaveformParams(
approx=approxSignal,
fmin=fminWavesSignal,
dist=factored_likelihood.distMpcRef * 1e6 *
lalsimutils.lsu_PC, # default distance
fref=fref)
Psig.m1 = lalsimutils.lsu_MSUN * opts.template_mass1
Psig.m2 = lalsimutils.lsu_MSUN * opts.template_mass2
Psig.tref = lal.LIGOTimeGPS(0.000000000) # Initialize as GPSTime object
Psig.tref += opts.force_gps_time # Pass value of float into it
# TEST THE SEGMENT LENGTH TARGET
if Psig:
f_temp = Psig.fmin
if Psig.fmin < 1e-2:
Psig.fmin = opts.fmin_SNR # necessary for duration estimate
print(" Min frequency for duration ... ", Psig.fmin)
Psig.print_params()
timeSegmentLength = lalsimutils.estimateWaveformDuration(
if rosUseRandomEventTime:
print(" --- Generating a random event (barycenter) time ---- ")
tEventFiducial += 0.05 * np.random.random_sample()
ampO = 0 # sets which modes to include in the physical signal
Lmax = 2 # sets which modes to include in the output
fref = 100
Psig = lalsimutils.ChooseWaveformParams(fmin=fminWaves,
radec=True,
incl=0.0,
phiref=0.0,
theta=0.2,
phi=0,
psi=0.0,
m1=m1,
m2=m2,
ampO=ampO,
approx=approxSignal,
fref=fref,
tref=theEpochFiducial + tEventFiducial,
deltaT=1. / fSample,
detector='H1',
dist=distanceFiducial * 1.e6 *
lal.LAL_PC_SI)
if rosUseRandomSkyLocation:
print(" --- Generating a random sky location ---- ")
Psig.theta = np.arccos(2 * (np.random.random_sample()) - 1)
Psig.phi = (np.random.random_sample()) * 2 * lal.LAL_PI
Psig.psi = (np.random.random_sample()) * lal.LAL_PI
if rosUseRandomSourceOrientation:
print(" --- Generating a random source orientation ---- ")
m1 = 4 * lal.LAL_MSUN_SI
m2 = 4 * lal.LAL_MSUN_SI
ampO = 0 # sets which modes to include in the physical signal
Lmax = 2 # sets which modes to include in the output
fref = 100
# Struct to hold template parameters
P = lsu.ChooseWaveformParams(fmin=fminWavesTemplate,
radec=False,
incl=0.0,
phiref=0.0,
theta=0.0,
phi=0,
psi=0.0,
m1=m1,
m2=m2,
ampO=ampO,
fref=fref,
tref=theEpochFiducial,
deltaT=1. / fSample,
dist=100 * 1.e6 * lal.LAL_PC_SI,
deltaF=df)
#
# Perform the Precompute stage
#
rholms_intp, crossTerms, rholms, epoch_post = factored_likelihood.PrecomputeLikelihoodTerms(
theEpochFiducial, P, data_dict, psd_dict, Lmax, analyticPSD_Q)
print("Finished Precomputation...")
print("====Generating metadata from precomputed results =====")