def likelihood_function(right_ascension, declination, phi_orb, inclination,
psi, distance):
# use EXTREMELY many bits
lnL = numpy.zeros(right_ascension.shape, dtype=numpy.float128)
# PRB: can we move this loop inside the factored_likelihood? It might help.
i = 0
# choose an array at the target sampling rate. P is inherited globally
for ph, th, phr, ic, ps, di in zip(right_ascension, declination,
phi_orb, inclination, psi,
distance):
P.phi = ph # right ascension
P.theta = th # declination
P.tref = fiducial_epoch # see 'tvals', above
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_time_marginalized(
tvals,
P,
rholms_intp,
rholms,
cross_terms,
det_epochs,
opts.l_max,
interpolate=opts.interpolate_time)
i += 1
return numpy.exp(lnL)
def likelihood_function(right_ascension, declination, phi_orb, inclination,
psi, distance):
# use EXTREMELY many bits
lnL = numpy.zeros(right_ascension.shape,dtype=numpy.float128)
# PRB: can we move this loop inside the factored_likelihood? It might help.
i = 0
# choose an array at the target sampling rate. P is inherited globally
for ph, th, phr, ic, ps, di in zip(right_ascension, declination,
phi_orb, inclination, psi, distance):
P.phi = ph # right ascension
P.theta = th # declination
P.tref = fiducial_epoch # see 'tvals', above
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_time_marginalized(tvals, P, rholms_intp, rholms, cross_terms, det_epochs, opts.l_max,interpolate=opts.interpolate_time)
i+=1
return numpy.exp(lnL)
def likelihood_function(right_ascension, declination, phi_orb, inclination,
psi, distance):
P.phi = right_ascension # right ascension
P.theta = declination # declination
P.tref = fiducial_epoch # see 'tvals', above
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_time_marginalized(
tvals,
P,
rholms_intp,
rholms,
cross_terms,
det_epochs,
opts.l_max,
interpolate=opts.interpolate_time)
return numpy.exp(lnL)
def likelihood_function(right_ascension, declination, phi_orb, inclination,
psi, distance):
'''
# FIXME - this is pretty bad
max_tpb = 1024 # Max number of threads per block for GPU: FIXME should be an --option
ntimes = len(rholms['H1'][(2,0)]) # FIXME this should be dynamic
nmodes = len(rholms['H1'].keys()) # FIXME this should be dynamic too
nclmns = numpy.int32( ntimes + max_tpb - (ntimes % max_tpb) ) # Number of cols to pad w/ 0s
nsamps = len(right_ascension)
'''
###
import math
right_ascension = numpy.linspace(0.1, math.pi, nsamps) + 1.0
declination = numpy.linspace(0.1, math.pi, nsamps) + 2.0
tref = numpy.linspace(0.1, math.pi, nsamps) + 3.0
phi_orb = numpy.linspace(0.1, math.pi, nsamps) + 4.0
inclination = numpy.linspace(0.1, math.pi, nsamps) + 5.0
psi = numpy.linspace(0.1, math.pi, nsamps) + 6.0
#distance = numpy.linspace(0.1, math.pi, nsamps) + 7.0
distance = numpy.ones(nsamps).astype(numpy.float64)
###
'''
right_ascension = right_ascension.astype(numpy.float64)
declination = declination.astype(numpy.float64)
tref = numpy.array([fiducial_epoch for item in right_ascension]).astype(numpy.float64)
phi_orb = phi_orb.astype(numpy.float64)
inclination = inclination.astype(numpy.float64)
psi = psi.astype(numpy.float64)
distance = distance.astype(numpy.float64)*1.e6*lal.PC_SI
# Pass a block of zeros onto the GPU to hold results from each detector
lnL_block = numpy.zeros((nsamps * nmodes, nclmns)).astype(numpy.float64)
lnL_block_gpu = gpuarray.to_gpu(lnL_block)
'''
for det in rholms.keys():
# Convert the crossterms to matrices
CTU = numpy.zeros(len(cross_termsU[det].keys()),
dtype=numpy.complex128)
CTV = numpy.zeros(len(cross_termsV[det].keys()),
dtype=numpy.complex128)
sort_terms_keys = sorted(cross_termsU[det],
key=lambda tup: (tup[0][1], tup[1][1]))
for i in range(0, len(cross_termsU[det].keys())):
CTU[i] = cross_termsU[det][sort_terms_keys[i]]
CTV[i] = cross_termsV[det][sort_terms_keys[i]]
side = numpy.sqrt(len(cross_termsU[det].keys()))
CTU = CTU.reshape((side, side))
CTV = CTV.reshape((side, side))
det_tns = numpy.array(
lalsim.DetectorPrefixToLALDetector(det).response).astype(
numpy.float64)
lnL_block_gpu += factored_likelihood.factored_log_likelihood_time_marginalized_gpu(
mod, right_ascension, declination, tref, phi_orb, inclination,
psi, distance, det_tns, rholms[det], CTU, CTV)
print("Marginalizing over Time... \n")
network_lnL_marg_gpu = factored_likelihood.marginalize_all_lnL(
mod, lnL_block_gpu, nmodes, nsamps, ntimes, nclmns,
tvals[1] - tvals[0])
print("Done marginalizing over time. \n")
# OLD STUFF
# use EXTREMELY many bits
lnL = numpy.zeros(right_ascension.shape, dtype=numpy.float128)
# PRB: can we move this loop inside the factored_likelihood? It might help.
i = 0
# choose an array at the target sampling rate. P is inherited globally
for ph, th, phr, ic, ps, di in zip(right_ascension, declination,
phi_orb, inclination, psi,
distance):
P.phi = ph # right ascension
P.theta = th # declination
P.tref = tref[i] #fiducial_epoch # see 'tvals', above
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_time_marginalized(
tvals,
P,
rholms_intp,
rholms,
cross_termsU,
cross_termsV,
det_epochs,
opts.l_max,
interpolate=opts.interpolate_time)
i += 1
import pdb
pdb.set_trace()
return numpy.exp(lnL)
def likelihood_function(right_ascension, declination, phi_orb, inclination,
psi, distance):
'''
# FIXME - this is pretty bad
max_tpb = 1024 # Max number of threads per block for GPU: FIXME should be an --option
ntimes = len(rholms['H1'][(2,0)]) # FIXME this should be dynamic
nmodes = len(rholms['H1'].keys()) # FIXME this should be dynamic too
nclmns = numpy.int32( ntimes + max_tpb - (ntimes % max_tpb) ) # Number of cols to pad w/ 0s
nsamps = len(right_ascension)
'''
###
import math
right_ascension = numpy.linspace(0.1, math.pi, nsamps) + 1.0
declination = numpy.linspace(0.1, math.pi, nsamps) + 2.0
tref = numpy.linspace(0.1, math.pi, nsamps) + 3.0
phi_orb = numpy.linspace(0.1, math.pi, nsamps) + 4.0
inclination = numpy.linspace(0.1, math.pi, nsamps) + 5.0
psi = numpy.linspace(0.1, math.pi, nsamps) + 6.0
#distance = numpy.linspace(0.1, math.pi, nsamps) + 7.0
distance = numpy.ones(nsamps).astype(numpy.float64)
###
'''
right_ascension = right_ascension.astype(numpy.float64)
declination = declination.astype(numpy.float64)
tref = numpy.array([fiducial_epoch for item in right_ascension]).astype(numpy.float64)
phi_orb = phi_orb.astype(numpy.float64)
inclination = inclination.astype(numpy.float64)
psi = psi.astype(numpy.float64)
distance = distance.astype(numpy.float64)*1.e6*lal.PC_SI
# Pass a block of zeros onto the GPU to hold results from each detector
lnL_block = numpy.zeros((nsamps * nmodes, nclmns)).astype(numpy.float64)
lnL_block_gpu = gpuarray.to_gpu(lnL_block)
'''
for det in rholms.keys():
# Convert the crossterms to matrices
CTU = numpy.zeros(len(cross_termsU[det].keys()), dtype=numpy.complex128)
CTV = numpy.zeros(len(cross_termsV[det].keys()), dtype=numpy.complex128)
sort_terms_keys = sorted(cross_termsU[det], key=lambda tup: (tup[0][1], tup[1][1]))
for i in range(0, len(cross_termsU[det].keys())):
CTU[i] = cross_termsU[det][sort_terms_keys[i]]
CTV[i] = cross_termsV[det][sort_terms_keys[i]]
side = numpy.sqrt(len(cross_termsU[det].keys()))
CTU = CTU.reshape((side, side))
CTV = CTV.reshape((side, side))
det_tns = numpy.array(lalsim.DetectorPrefixToLALDetector(det).response).astype(numpy.float64)
lnL_block_gpu += factored_likelihood.factored_log_likelihood_time_marginalized_gpu(mod, right_ascension, declination, tref, phi_orb, inclination, psi, distance, det_tns, rholms[det], CTU, CTV)
print("Marginalizing over Time... \n")
network_lnL_marg_gpu = factored_likelihood.marginalize_all_lnL(mod, lnL_block_gpu, nmodes, nsamps, ntimes, nclmns, tvals[1]-tvals[0])
print("Done marginalizing over time. \n")
# OLD STUFF
# use EXTREMELY many bits
lnL = numpy.zeros(right_ascension.shape,dtype=numpy.float128)
# PRB: can we move this loop inside the factored_likelihood? It might help.
i = 0
# choose an array at the target sampling rate. P is inherited globally
for ph, th, phr, ic, ps, di in zip(right_ascension, declination,
phi_orb, inclination, psi, distance):
P.phi = ph # right ascension
P.theta = th # declination
P.tref = tref[i] #fiducial_epoch # see 'tvals', above
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_time_marginalized(tvals, P, rholms_intp, rholms, cross_termsU, cross_termsV, det_epochs, opts.l_max,interpolate=opts.interpolate_time)
i+=1
import pdb
pdb.set_trace()
return numpy.exp(lnL)
