def likelihood_function(right_ascension, declination, t_ref, phi_orb,
inclination, psi, distance):
P.phi = right_ascension # right ascension
P.theta = declination # declination
P.tref = t_ref + fiducial_epoch # ref. time (rel to epoch for data taking)
P.phiref = phi_orb # ref. orbital phase
P.incl = inclination # inclination
P.psi = psi # polarization angle
P.dist = distance * 1.e6 * lal.PC_SI # luminosity distance
lnL = factored_likelihood.factored_log_likelihood(
P, rholms_intp, cross_terms, opts.l_max)
return numpy.exp(lnL)
def likelihood_function(right_ascension, declination, t_ref, phi_orb, inclination, psi, distance):
# use EXTREMELY many bits
lnL = numpy.zeros(right_ascension.shape,dtype=numpy.float128)
i = 0
for ph, th, tr, phr, ic, ps, di in zip(right_ascension, declination,
t_ref, phi_orb, inclination, psi, distance):
P.phi = ph # right ascension
P.theta = th # declination
P.tref = fiducial_epoch + tr # ref. time (rel to epoch for data taking)
P.phiref = phr # ref. orbital phase
P.incl = ic # inclination
P.psi = ps # polarization angle
P.dist = di* 1.e6 * lal.PC_SI # luminosity distance
lnL[i] = factored_likelihood.factored_log_likelihood(P, rholms_intp, cross_terms, opts.l_max)
i+=1
return numpy.exp(lnL)
def likelihood_function(right_ascension, declination, t_ref, phi_orb, inclination, psi, distance):
# use EXTREMELY many bits
lnL = numpy.zeros(right_ascension.shape,dtype=numpy.float128)
i = 0
for ph, th, tr, phr, ic, ps, di in zip(right_ascension, declination,
t_ref, phi_orb, inclination, psi, distance):
P.phi = ph # right ascension
P.theta = th # declination
P.tref = fiducial_epoch + tr # ref. time (rel to epoch for data taking)
P.phiref = phr # ref. orbital phase
P.incl = ic # inclination
P.psi = ps # polarization angle
P.dist = di* 1.e6 * lal.PC_SI # luminosity distance
lnL[i] = factored_likelihood.factored_log_likelihood(P, rholms_intp, cross_terms, opts.l_max)
i+=1
return numpy.exp(lnL)
t_window = 0.15
rholms_intp, cross_terms, rholms = factored_likelihood.precompute_likelihood_terms(fiducial_epoch, t_window, P, data_dict, psd_dict, opts.l_max, fmax, False, inv_spec_trunc_Q, T_spec)
if opts.pin_to_sim and not opts.zero_noise:
P.copy_lsctables_sim_inspiral(sim_row)
print "Pinned parameters from sim_inspiral"
print "\tRA", P.phi, sim_row.longitude
print "\tdec", P.theta, sim_row.latitude
print "\tt ref %d.%d" % (P.tref.gpsSeconds, P.tref.gpsNanoSeconds), sim_row.get_time_geocent()
print "\torb phase", P.phiref, sim_row.coa_phase # ref. orbital phase
print "\tinclination", P.incl, sim_row.inclination # inclination
print "\tpsi", P.psi, sim_row.polarization # polarization angle
print "\tdistance", P.dist/(1e6 * lal.PC_SI), sim_row.distance # luminosity distance
logL = factored_likelihood.factored_log_likelihood(P, rholms_intp, cross_terms, opts.l_max)
print "Pinned log likelihood: %g, (%g in \"SNR\")" % (logL, numpy.sqrt(2*logL))
tref = float(P.tref)
tvals = numpy.arange(tref-0.01, tref+0.01, 0.00001)
logLs = []
for t in tvals:
P.tref = lal.LIGOTimeGPS(t)
logLs.append(factored_likelihood.factored_log_likelihood(P, rholms_intp, cross_terms, opts.l_max))
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot
print "Maximum logL is %g, (%g in \"SNR\")" % (max(logLs), numpy.sqrt(2*max(logLs)))
print "Which occurs at sample", numpy.argmax(logLs)
print "This corresponds to time %.20g" % tvals[numpy.argmax(logLs)]
print "The data event time is: %.20g" % sim_row.get_time_geocent()
print "Difference from geocenter t_ref is %.20g" %\
if opts.pin_to_sim:
P.copy_lsctables_sim_inspiral(sim_row)
print "Pinned parameters from sim_inspiral"
print "\tRA", P.phi, sim_row.longitude
print "\tdec", P.theta, sim_row.latitude
print "\tt ref %d.%d" % (
P.tref.gpsSeconds, P.tref.gpsNanoSeconds), sim_row.get_time_geocent()
print "\torb phase", P.phiref, sim_row.coa_phase # ref. orbital phase
print "\tinclination", P.incl, sim_row.inclination # inclination
print "\tpsi", P.psi, sim_row.polarization # polarization angle
print "\tdistance", P.dist / (
1e6 * lal.PC_SI), sim_row.distance # luminosity distance
logL = factored_likelihood.factored_log_likelihood(P, rholms_intp,
cross_termsU,
opts.l_max)
print "Pinned log likelihood: %g, (%g in \"SNR\")" % (logL,
numpy.sqrt(2 * logL))
tref = float(P.tref)
tvals = numpy.arange(tref - 0.01, tref + 0.01, 0.00001)
logLs = []
for t in tvals:
P.tref = lal.LIGOTimeGPS(t)
logLs.append(
factored_likelihood.factored_log_likelihood(
P, rholms_intp, cross_termsU, opts.l_max))
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot
print "Maximum logL is %g, (%g in \"SNR\")" % (max(logLs),
t_window = 0.15
rholms_intp, cross_terms, rholms = factored_likelihood.precompute_likelihood_terms(fiducial_epoch, t_window, P, data_dict, psd_dict, opts.l_max, fmax, False, inv_spec_trunc_Q, T_spec)
if opts.pin_to_sim and not opts.zero_noise:
P.copy_lsctables_sim_inspiral(sim_row)
print "Pinned parameters from sim_inspiral"
print "\tRA", P.phi, sim_row.longitude
print "\tdec", P.theta, sim_row.latitude
print "\tt ref %d.%d" % (P.tref.gpsSeconds, P.tref.gpsNanoSeconds), sim_row.get_time_geocent()
print "\torb phase", P.phiref, sim_row.coa_phase # ref. orbital phase
print "\tinclination", P.incl, sim_row.inclination # inclination
print "\tpsi", P.psi, sim_row.polarization # polarization angle
print "\tdistance", P.dist/(1e6 * lal.PC_SI), sim_row.distance # luminosity distance
logL = factored_likelihood.factored_log_likelihood(P, rholms_intp, cross_terms, opts.l_max)
print "Pinned log likelihood: %g, (%g in \"SNR\")" % (logL, numpy.sqrt(2*logL))
tref = float(P.tref)
tvals = numpy.arange(tref-0.01, tref+0.01, 0.00001)
logLs = []
for t in tvals:
P.tref = lal.LIGOTimeGPS(t)
logLs.append(factored_likelihood.factored_log_likelihood(P, rholms_intp, cross_terms, opts.l_max))
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot
print "Maximum logL is %g, (%g in \"SNR\")" % (max(logLs), numpy.sqrt(2*max(logLs)))
print "Which occurs at sample", numpy.argmax(logLs)
print "This corresponds to time %.20g" % tvals[numpy.argmax(logLs)]
print "The data event time is: %.20g" % sim_row.get_time_geocent()
print "Difference from geocenter t_ref is %.20g" %\
