def generate(self, **kwargs):
"""Generates a waveform, applies a time shift and the detector response
function from the given kwargs.
"""
self.current_params.update(kwargs)
rfparams = {
param: self.current_params[param]
for param in kwargs if param not in self.location_args
}
hp, hc = self.rframe_generator.generate(**rfparams)
if isinstance(hp, TimeSeries):
df = self.current_params['delta_f']
hp = hp.to_frequencyseries(delta_f=df)
hc = hc.to_frequencyseries(delta_f=df)
# time-domain waveforms will not be shifted so that the peak amp
# happens at the end of the time series (as they are for f-domain),
# so we add an additional shift to account for it
tshift = 1. / df - abs(hp._epoch)
else:
tshift = 0.
hp._epoch = hc._epoch = self._epoch
h = {}
if self.detector_names != ['RF']:
for detname, det in self.detectors.items():
# apply detector response function
fp, fc = det.antenna_pattern(
self.current_params['ra'], self.current_params['dec'],
self.current_params['polarization'],
self.current_params['tc'])
thish = fp * hp + fc * hc
# apply the time shift
tc = self.current_params['tc'] + \
det.time_delay_from_earth_center(self.current_params['ra'],
self.current_params['dec'], self.current_params['tc'])
h[detname] = apply_fd_time_shift(thish,
tc + tshift,
copy=False)
if self.recalib:
# recalibrate with given calibration model
h[detname] = \
self.recalib[detname].map_to_adjust(h[detname],
**self.current_params)
else:
# no detector response, just use the + polarization
if 'tc' in self.current_params:
hp = apply_fd_time_shift(hp,
self.current_params['tc'] + tshift,
copy=False)
h['RF'] = hp
if self.gates is not None:
# resize all to nearest power of 2
for d in h.values():
d.resize(ceilpow2(len(d) - 1) + 1)
h = strain.apply_gates_to_fd(h, self.gates)
return h
def generate(self, **kwargs):
"""Generates a waveform, applies a time shift and the detector response
function from the given kwargs.
"""
self.current_params.update(kwargs)
rfparams = {param: self.current_params[param]
for param in kwargs if param not in self.location_args}
hp, hc = self.rframe_generator.generate(**rfparams)
if isinstance(hp, TimeSeries):
df = self.current_params['delta_f']
hp = hp.to_frequencyseries(delta_f=df)
hc = hc.to_frequencyseries(delta_f=df)
# time-domain waveforms will not be shifted so that the peak amp
# happens at the end of the time series (as they are for f-domain),
# so we add an additional shift to account for it
tshift = 1./df - abs(hp._epoch)
else:
tshift = 0.
hp._epoch = hc._epoch = self._epoch
h = {}
if self.detector_names != ['RF']:
for detname, det in self.detectors.items():
# apply detector response function
fp, fc = det.antenna_pattern(self.current_params['ra'],
self.current_params['dec'],
self.current_params['polarization'],
self.current_params['tc'])
thish = fp*hp + fc*hc
# apply the time shift
tc = self.current_params['tc'] + \
det.time_delay_from_earth_center(self.current_params['ra'],
self.current_params['dec'], self.current_params['tc'])
h[detname] = apply_fd_time_shift(thish, tc+tshift, copy=False)
if self.recalib:
# recalibrate with given calibration model
h[detname] = \
self.recalib[detname].map_to_adjust(h[detname],
**self.current_params)
else:
# no detector response, just use the + polarization
if 'tc' in self.current_params:
hp = apply_fd_time_shift(hp, self.current_params['tc']+tshift,
copy=False)
h['RF'] = hp
if self.gates is not None:
# resize all to nearest power of 2
for d in h.values():
d.resize(ceilpow2(len(d)-1) + 1)
h = gate.apply_gates_to_fd(h, self.gates)
return h
def generate(self, **kwargs):
"""Generates and returns a waveform decompsed into separate modes.
Returns
-------
dict :
Dictionary of ``detector names -> modes -> (ulm, vlm)``, where
``ulm, vlm`` are the frequency-domain representations of the real
and imaginary parts, respectively, of the complex time series
representation of the ``hlm``.
"""
self.current_params.update(kwargs)
rfparams = {param: self.current_params[param]
for param in kwargs if param not in self.location_args}
hlms = self.rframe_generator.generate(**rfparams)
h = {det: {} for det in self.detectors}
for mode in hlms:
ulm, vlm = hlms[mode]
if isinstance(ulm, TimeSeries):
df = self.current_params['delta_f']
ulm = ulm.to_frequencyseries(delta_f=df)
vlm = vlm.to_frequencyseries(delta_f=df)
# time-domain waveforms will not be shifted so that the peak
# amplitude happens at the end of the time series (as they are
# for f-domain), so we add an additional shift to account for
# it
tshift = 1./df - abs(ulm._epoch)
else:
tshift = 0.
ulm._epoch = vlm._epoch = self._epoch
if self.detector_names != ['RF']:
for detname, det in self.detectors.items():
# apply the time shift
tc = self.current_params['tc'] + \
det.time_delay_from_earth_center(
self.current_params['ra'],
self.current_params['dec'],
self.current_params['tc'])
detulm = apply_fd_time_shift(ulm, tc+tshift, copy=True)
detvlm = apply_fd_time_shift(vlm, tc+tshift, copy=True)
if self.recalib:
# recalibrate with given calibration model
detulm = self.recalib[detname].map_to_adjust(
detulm, **self.current_params)
detvlm = self.recalib[detname].map_to_adjust(
detvlm, **self.current_params)
h[detname][mode] = (detulm, detvlm)
else:
# no detector response, just apply time shift
if 'tc' in self.current_params:
ulm = apply_fd_time_shift(ulm,
self.current_params['tc']+tshift,
copy=False)
vlm = apply_fd_time_shift(vlm,
self.current_params['tc']+tshift,
copy=False)
h['RF'][mode] = (ulm, vlm)
if self.gates is not None:
# resize all to nearest power of 2
ulms = {}
vlms = {}
for det in h:
ulm, vlm = h[det][mode]
ulm.resize(ceilpow2(len(ulm)-1) + 1)
vlm.resize(ceilpow2(len(vlm)-1) + 1)
ulms[det] = ulm
vlms[det] = vlm
ulms = strain.apply_gates_to_fd(ulms, self.gates)
vlms = strain.apply_gates_to_fd(ulms, self.gates)
for det in ulms:
h[det][mode] = (ulms[det], vlms[det])
return h
def generate(self, **kwargs):
"""Generates a waveform polarizations and applies a time shift.
Returns
-------
dict :
Dictionary of ``detector names -> (hp, hc)``, where ``hp, hc`` are
the plus and cross polarization, respectively.
"""
self.current_params.update(kwargs)
rfparams = {param: self.current_params[param]
for param in kwargs if param not in self.location_args}
hp, hc = self.rframe_generator.generate(**rfparams)
if isinstance(hp, TimeSeries):
df = self.current_params['delta_f']
hp = hp.to_frequencyseries(delta_f=df)
hc = hc.to_frequencyseries(delta_f=df)
# time-domain waveforms will not be shifted so that the peak amp
# happens at the end of the time series (as they are for f-domain),
# so we add an additional shift to account for it
tshift = 1./df - abs(hp._epoch)
else:
tshift = 0.
hp._epoch = hc._epoch = self._epoch
h = {}
if self.detector_names != ['RF']:
for detname, det in self.detectors.items():
# apply the time shift
tc = self.current_params['tc'] + \
det.time_delay_from_earth_center(self.current_params['ra'],
self.current_params['dec'], self.current_params['tc'])
dethp = apply_fd_time_shift(hp, tc+tshift, copy=True)
dethc = apply_fd_time_shift(hc, tc+tshift, copy=True)
if self.recalib:
# recalibrate with given calibration model
dethp = self.recalib[detname].map_to_adjust(
dethp, **self.current_params)
dethc = self.recalib[detname].map_to_adjust(
dethc, **self.current_params)
h[detname] = (dethp, dethc)
else:
# no detector response, just use the + polarization
if 'tc' in self.current_params:
hp = apply_fd_time_shift(hp, self.current_params['tc']+tshift,
copy=False)
hc = apply_fd_time_shift(hc, self.current_params['tc']+tshift,
copy=False)
h['RF'] = (hp, hc)
if self.gates is not None:
# resize all to nearest power of 2
hps = {}
hcs = {}
for det in h:
hp = h[det]
hc = h[det]
hp.resize(ceilpow2(len(hp)-1) + 1)
hc.resize(ceilpow2(len(hc)-1) + 1)
hps[det] = hp
hcs[det] = hc
hps = strain.apply_gates_to_fd(hps, self.gates)
hcs = strain.apply_gates_to_fd(hps, self.gates)
h = {det: (hps[det], hcs[det]) for det in h}
return h
def spa_tmplt(**kwds):
""" Generate a minimal TaylorF2 approximant with optimations for the sin/cos
"""
# Pull out the input arguments
f_lower = kwds['f_lower']
delta_f = kwds['delta_f']
distance = kwds['distance']
mass1 = kwds['mass1']
mass2 = kwds['mass2']
s1z = kwds['spin1z']
s2z = kwds['spin2z']
phase_order = int(kwds['phase_order'])
#amplitude_order = int(kwds['amplitude_order'])
spin_order = int(kwds['spin_order'])
if 'out' in kwds:
out = kwds['out']
else:
out = None
amp_factor = spa_amplitude_factor(mass1=mass1, mass2=mass2) / distance
lal_pars = lal.CreateDict()
if phase_order != -1:
lalsimulation.SimInspiralWaveformParamsInsertPNPhaseOrder(
lal_pars, phase_order)
if spin_order != -1:
lalsimulation.SimInspiralWaveformParamsInsertPNSpinOrder(
lal_pars, spin_order)
#Calculate the PN terms
phasing = lalsimulation.SimInspiralTaylorF2AlignedPhasing(
float(mass1), float(mass2), float(s1z), float(s2z), lal_pars)
pfaN = phasing.v[0]
pfa2 = phasing.v[2] / pfaN
pfa3 = phasing.v[3] / pfaN
pfa4 = phasing.v[4] / pfaN
pfa5 = phasing.v[5] / pfaN
pfa6 = (phasing.v[6] - phasing.vlogv[6] * log(4)) / pfaN
pfa7 = phasing.v[7] / pfaN
pfl5 = phasing.vlogv[5] / pfaN
pfl6 = phasing.vlogv[6] / pfaN
piM = lal.PI * (mass1 + mass2) * lal.MTSUN_SI
kmin = int(f_lower / float(delta_f))
vISCO = 1. / sqrt(6.)
fISCO = vISCO * vISCO * vISCO / piM
kmax = int(fISCO / delta_f)
f_max = ceilpow2(fISCO)
n = int(f_max / delta_f) + 1
if not out:
htilde = FrequencySeries(zeros(n, dtype=numpy.complex64),
delta_f=delta_f,
copy=False)
else:
if type(out) is not Array:
raise TypeError("Output must be an instance of Array")
if len(out) < kmax:
kmax = len(out)
if out.dtype != complex64:
raise TypeError("Output array is the wrong dtype")
htilde = FrequencySeries(out, delta_f=delta_f, copy=False)
spa_tmplt_engine(htilde[kmin:kmax], kmin, phase_order, delta_f, piM, pfaN,
pfa2, pfa3, pfa4, pfa5, pfl5, pfa6, pfl6, pfa7,
amp_factor)
return htilde
def spintaylorf2(**kwds):
""" Return a SpinTaylorF2 waveform using CUDA to generate the phase and amplitude
"""
#####Pull out the input arguments#####
f_lower = double(kwds['f_lower'])
delta_f = double(kwds['delta_f'])
distance = double(kwds['distance'])
mass1 = double(kwds['mass1'])
mass2 = double(kwds['mass2'])
spin1x = double(kwds['spin1x'])
spin1y = double(kwds['spin1y'])
spin1z = double(kwds['spin1z'])
phi0 = double(kwds['coa_phase']) #Orbital Phase at coalescence
phase_order = int(kwds['phase_order'])
amplitude_order = int(kwds['amplitude_order'])
inclination = double(kwds['inclination'])
lnhatx = sin(inclination)
lnhaty = 0.
lnhatz = cos(inclination)
psi = 0.
tC= -1.0 / delta_f
M = mass1 + mass2
eta = mass1 * mass2 / (M * M)
m_sec = M * lal.MTSUN_SI
piM = lal.PI * m_sec
vISCO = 1. / sqrt(6.)
fISCO = vISCO * vISCO * vISCO / piM
f_max = ceilpow2(fISCO)
n = int(f_max / delta_f + 1)
kmax = int(fISCO / delta_f)
kmin = int(numpy.ceil(f_lower / delta_f))
kmax = kmax if (kmax<n) else n
#####Calculate the Orientation#####
v0 = pow(piM * kmin * delta_f,1./3)
chi = sqrt(spin1x**2+spin1y**2+spin1z**2)
kappa = (lnhatx*spin1x+lnhaty*spin1y+lnhatz*spin1z)/chi if (chi > 0.) else 1.
Jx0 = mass1*mass2*lnhatx/v0 + mass1*mass1*spin1x
Jy0 = mass1*mass2*lnhaty/v0 + mass1*mass1*spin1y
Jz0 = mass1*mass2*lnhatz/v0 + mass1*mass1*spin1z
thetaJ = acos(Jz0 / sqrt(Jx0**2+Jy0**2+Jz0**2))
psiJ = atan2(Jy0, -Jx0) # FIXME: check that Jy0 and Jx0 are not both 0
# Rotate Lnhat back to frame where J is along z, to figure out initial alpha
rotLx = lnhatx*cos(thetaJ)*cos(psiJ) - lnhaty*cos(thetaJ)*sin(psiJ) + lnhatz*sin(thetaJ)
rotLy = lnhatx*sin(psiJ) + lnhaty*cos(psiJ)
alpha0 = atan2(rotLy, rotLx) # FIXME: check that rotLy and rotLx are not both 0
psiJ_P =psiJ + psi
psiJ_C =psiJ + psi + lal.PI/4.
#####Calculate the Coefficients#####
quadparam = 1.
gamma0 = mass1*chi/mass2
#Calculate the spin corrections
# FIXME should use pycbc's function, but sigma has different expression
# in Andy's code, double check
# pn_beta, pn_sigma, pn_gamma = pycbc.pnutils.mass1_mass2_spin1z_spin2z_to_beta_sigma_gamma(
# mass1, mass2, chi*kappa, 0) # FIXME: spin2 is taken to be 0
pn_beta = (113.*mass1/(12.*M) - 19.*eta/6.)*chi*kappa
pn_sigma = ( (5.*(3.*kappa*kappa-1.)/2.) + (7. - kappa*kappa)/96. ) * (mass1*mass1*chi*chi/M/M)
pn_gamma = (5.*(146597. + 7056.*eta)*mass1/(2268.*M) - 10.*eta*(1276. + 153.*eta)/81.)*chi*kappa
prec_fac0 = 5.*(4. + 3.*mass2/mass1)/64.
dtdv2 = 743./336. + 11.*eta/4.
dtdv3 = -4.*lal.PI + pn_beta
dtdv4 = 3058673./1016064. + 5429.*eta/1008. + 617.*eta*eta/144. - pn_sigma
dtdv5 = (-7729./672.+13.*eta/8.)*lal.PI + 9.*pn_gamma/40.
#####Calculate the Initial Euler Angles alpha_ref, beta_ref=0 and zeta_ref#####
gam = gamma0*v0
sqrtfac = sqrt(1. + 2.*kappa*gam + gam*gam)
logv0 = log(v0)
logfac1 = log(1. + kappa*gam + sqrtfac)
logfac2 = log(kappa + gam + sqrtfac)
v02 = v0 * v0
v03 = v0 * v02
kappa2 = kappa * kappa
kappa3 = kappa2 * kappa
gamma02 = gamma0 * gamma0
gamma03 = gamma02 *gamma0
alpha_ref =prec_fac0*( logfac2 *( dtdv2*gamma0 + dtdv3*kappa - dtdv5*kappa/(2.*gamma02) + dtdv4/(2.*gamma0) - dtdv4*kappa2/(2.*gamma0) + (dtdv5*kappa3)/(2.*gamma02) ) + logfac1*( - dtdv2*gamma0*kappa - dtdv3 + kappa*gamma03/2. - gamma03*kappa3/2. ) + logv0 *( dtdv2*gamma0*kappa + dtdv3 - kappa*gamma03/2. + gamma03*kappa3/2. ) + sqrtfac *( dtdv3 + dtdv4*v0/2. + dtdv5/gamma02/3. + dtdv4*kappa/(2.*gamma0) + dtdv5*kappa*v0/(6.*gamma0) - dtdv5*kappa2/(2.*gamma02) - 1/(3.*v03) - gamma0*kappa/(6.*v02) - dtdv2/v0 - gamma02/(3.*v0) + gamma02*kappa2/(2.*v0) + dtdv5*v02/3. )) - alpha0
zeta_ref = prec_fac0*( dtdv3*gamma0*kappa*v0 + dtdv4*v0 + logfac2 *(-dtdv2*gamma0 - dtdv3*kappa + dtdv5*kappa/(2.*gamma02) - dtdv4/(2.*gamma0) + dtdv4*kappa2/(2.*gamma0) - dtdv5*kappa3/(2.*gamma02) ) + logv0 *( kappa*gamma03/2. - gamma03*kappa3/2. ) + logfac1 *( dtdv2*gamma0*kappa + dtdv3 - kappa*gamma03/2. + gamma03*kappa3/2. ) - 1/(3.*v03) - gamma0*kappa/(2.*v02) - dtdv2/v0 + dtdv4*gamma0*kappa*v02/2. + dtdv5*v02/2. + sqrtfac *( -dtdv3 - dtdv4*v0/2. - dtdv5/(3.*gamma02) - dtdv4*kappa/(2.*gamma0) - dtdv5*kappa*v0/(6.*gamma0) + dtdv5*kappa2/(2.*gamma02) + 1/(3.*v03) + gamma0*kappa/(6.*v02) + dtdv2/v0 + gamma02/(3.*v0) - gamma02*kappa2/(2.*v0) - dtdv5*v02/3. ) + dtdv5*gamma0*kappa*v03/3. )
#####Calculate the Complex sideband factors, mm=2 is first entry#####
RE_SBfac0= (1.+cos(thetaJ)**2)/2.
RE_SBfac1= sin(2.*thetaJ)
RE_SBfac2= 3.*sin(thetaJ)**2
RE_SBfac3= -sin(2.*thetaJ)
RE_SBfac4= (1.+cos(thetaJ)**2)/2.
IM_SBfac0= -cos(thetaJ)
IM_SBfac1= -2.*sin(thetaJ)
IM_SBfac2= 0.
IM_SBfac3= -2.*sin(thetaJ)
IM_SBfac4= cos(thetaJ)
#####Calculate the PN terms # FIXME replace with functions in lalsimulation #####
theta = -11831./9240.
lambdaa = -1987./3080.0
pfaN = 3.0/(128.0 * eta)
pfa2 = 5.0*(743.0/84 + 11.0 * eta)/9.0
pfa3 = -16.0*lal.PI + 4.0*pn_beta
pfa4 = 5.0*(3058.673/7.056 + 5429.0/7.0 * eta + 617.0 * eta*eta)/72.0 - \
10.0*pn_sigma
pfa5 = 5.0/9.0 * (7729.0/84.0 - 13.0 * eta) * lal.PI - pn_gamma
pfl5 = 5.0/3.0 * (7729.0/84.0 - 13.0 * eta) * lal.PI - pn_gamma * 3
pfa6 = (11583.231236531/4.694215680 - 640.0/3.0 * lal.PI * lal.PI- \
6848.0/21.0*lal.GAMMA) + \
eta * (-15335.597827/3.048192 + 2255./12. * lal.PI * \
lal.PI - 1760./3.*theta +12320./9.*lambdaa) + \
eta*eta * 76055.0/1728.0 - \
eta*eta*eta* 127825.0/1296.0
pfl6 = -6848.0/21.0
pfa7 = lal.PI * 5.0/756.0 * ( 15419335.0/336.0 + 75703.0/2.0 * eta - \
14809.0 * eta*eta)
FTaN = 32.0 * eta*eta / 5.0
FTa2 = -(12.47/3.36 + 3.5/1.2 * eta)
FTa3 = 4.0 * lal.PI
FTa4 = -(44.711/9.072 - 92.71/5.04 * eta - 6.5/1.8 * eta*eta)
FTa5 = -(81.91/6.72 + 58.3/2.4 * eta) * lal.PI
FTa6 = (664.3739519/6.9854400 + 16.0/3.0 * lal.PI*lal.PI -
17.12/1.05 * lal.GAMMA +
(4.1/4.8 * lal.PI*lal.PI - 134.543/7.776) * eta -
94.403/3.024 * eta*eta - 7.75/3.24 * eta*eta*eta)
FTl6 = -8.56/1.05
FTa7 = -(162.85/5.04 - 214.745/1.728 * eta - 193.385/3.024 * eta*eta) \
* lal.PI
dETaN = 2 * -eta/2.0
dETa1 = 2 * -(3.0/4.0 + 1.0/12.0 * eta)
dETa2 = 3 * -(27.0/8.0 - 19.0/8.0 * eta + 1./24.0 * eta*eta)
dETa3 = 4 * -(67.5/6.4 - (344.45/5.76 - 20.5/9.6 * lal.PI*lal.PI) *
eta + 15.5/9.6 * eta*eta + 3.5/518.4 * eta*eta*eta)
amp0 = -4. * mass1 * mass2 / (1.0e+06 * distance * lal.PC_SI ) * \
lal.MRSUN_SI * lal.MTSUN_SI * sqrt(lal.PI/12.0)
htildeP = FrequencySeries(zeros(n,dtype=complex128), delta_f=delta_f, copy=False)
htildeC = FrequencySeries(zeros(n,dtype=complex128), delta_f=delta_f, copy=False)
spintaylorf2_kernel(htildeP.data[kmin:kmax], htildeC.data[kmin:kmax],
kmin, phase_order, amplitude_order, delta_f, piM, pfaN,
pfa2, pfa3, pfa4, pfa5, pfl5,
pfa6, pfl6, pfa7, FTaN, FTa2,
FTa3, FTa4, FTa5, FTa6,
FTl6, FTa7, dETaN, dETa1, dETa2, dETa3,
amp0, tC, phi0,
kappa, prec_fac0, alpha_ref, zeta_ref,
dtdv2, dtdv3, dtdv4, dtdv5,
RE_SBfac0, RE_SBfac1, RE_SBfac2, RE_SBfac3, RE_SBfac4,
IM_SBfac0, IM_SBfac1, IM_SBfac2, IM_SBfac3, IM_SBfac4,
psiJ_P, psiJ_C, gamma0)
return htildeP, htildeC
def spa_tmplt(**kwds):
""" Generate a minimal TaylorF2 approximant with optimations for the sin/cos
"""
# Pull out the input arguments
f_lower = kwds['f_lower']
delta_f = kwds['delta_f']
distance = kwds['distance']
mass1 = kwds['mass1']
mass2 = kwds['mass2']
s1z = kwds['spin1z']
s2z = kwds['spin2z']
phase_order = int(kwds['phase_order'])
amplitude_order = int(kwds['amplitude_order'])
spin_order = int(kwds['spin_order'])
if 'out' in kwds:
out = kwds['out']
else:
out = None
amp_factor = spa_amplitude_factor(mass1=mass1, mass2=mass2) / distance
lal_pars = lal.CreateDict()
if phase_order != -1:
lalsimulation.SimInspiralWaveformParamsInsertPNPhaseOrder(
lal_pars, phase_order)
if spin_order != -1:
lalsimulation.SimInspiralWaveformParamsInsertPNSpinOrder(
lal_pars, spin_order)
#Calculate the PN terms
phasing = lalsimulation.SimInspiralTaylorF2AlignedPhasing(
float(mass1), float(mass2),
float(s1z), float(s2z),
lal_pars)
pfaN = phasing.v[0]
pfa2 = phasing.v[2] / pfaN
pfa3 = phasing.v[3] / pfaN
pfa4 = phasing.v[4] / pfaN
pfa5 = phasing.v[5] / pfaN
pfa6 = (phasing.v[6] - phasing.vlogv[6] * log(4)) / pfaN
pfa7 = phasing.v[7] / pfaN
pfl5 = phasing.vlogv[5] / pfaN
pfl6 = phasing.vlogv[6] / pfaN
piM = lal.PI * (mass1 + mass2) * lal.MTSUN_SI
kmin = int(f_lower / float(delta_f))
vISCO = 1. / sqrt(6.)
fISCO = vISCO * vISCO * vISCO / piM
kmax = int(fISCO / delta_f)
f_max = ceilpow2(fISCO)
n = int(f_max / delta_f) + 1
if not out:
htilde = FrequencySeries(zeros(n, dtype=numpy.complex64), delta_f=delta_f, copy=False)
else:
if type(out) is not Array:
raise TypeError("Output must be an instance of Array")
if len(out) < kmax:
kmax = len(out)
if out.dtype != complex64:
raise TypeError("Output array is the wrong dtype")
htilde = FrequencySeries(out, delta_f=delta_f, copy=False)
spa_tmplt_engine(htilde[kmin:kmax], kmin, phase_order, delta_f, piM, pfaN,
pfa2, pfa3, pfa4, pfa5, pfl5,
pfa6, pfl6, pfa7, amp_factor)
return htilde
def spintaylorf2(**kwds):
""" Return a SpinTaylorF2 waveform using CUDA to generate the phase and amplitude
"""
#####Pull out the input arguments#####
f_lower = double(kwds['f_lower'])
delta_f = double(kwds['delta_f'])
distance = double(kwds['distance'])
mass1 = double(kwds['mass1'])
mass2 = double(kwds['mass2'])
spin1x = double(kwds['spin1x'])
spin1y = double(kwds['spin1y'])
spin1z = double(kwds['spin1z'])
phi0 = double(kwds['coa_phase']) #Orbital Phase at coalescence
phase_order = int(kwds['phase_order'])
amplitude_order = int(kwds['amplitude_order'])
inclination = double(kwds['inclination'])
lnhatx = sin(inclination)
lnhaty = 0.
lnhatz = cos(inclination)
psi = 0.
tC = -1.0 / delta_f
M = mass1 + mass2
eta = mass1 * mass2 / (M * M)
m_sec = M * lal.MTSUN_SI
piM = lal.PI * m_sec
vISCO = 1. / sqrt(6.)
fISCO = vISCO * vISCO * vISCO / piM
f_max = ceilpow2(fISCO)
n = int(f_max / delta_f + 1)
kmax = int(fISCO / delta_f)
kmin = int(numpy.ceil(f_lower / delta_f))
kmax = kmax if (kmax < n) else n
#####Calculate the Orientation#####
v0 = pow(piM * kmin * delta_f, 1. / 3)
chi = sqrt(spin1x**2 + spin1y**2 + spin1z**2)
kappa = (lnhatx * spin1x + lnhaty * spin1y + lnhatz * spin1z) / chi if (
chi > 0.) else 1.
Jx0 = mass1 * mass2 * lnhatx / v0 + mass1 * mass1 * spin1x
Jy0 = mass1 * mass2 * lnhaty / v0 + mass1 * mass1 * spin1y
Jz0 = mass1 * mass2 * lnhatz / v0 + mass1 * mass1 * spin1z
thetaJ = acos(Jz0 / sqrt(Jx0**2 + Jy0**2 + Jz0**2))
psiJ = atan2(Jy0, -Jx0) # FIXME: check that Jy0 and Jx0 are not both 0
# Rotate Lnhat back to frame where J is along z, to figure out initial alpha
rotLx = lnhatx * cos(thetaJ) * cos(psiJ) - lnhaty * cos(thetaJ) * sin(
psiJ) + lnhatz * sin(thetaJ)
rotLy = lnhatx * sin(psiJ) + lnhaty * cos(psiJ)
alpha0 = atan2(rotLy,
rotLx) # FIXME: check that rotLy and rotLx are not both 0
psiJ_P = psiJ + psi
psiJ_C = psiJ + psi + lal.PI / 4.
#####Calculate the Coefficients#####
#quadparam = 1.
gamma0 = mass1 * chi / mass2
#Calculate the spin corrections
# FIXME should use pycbc's function, but sigma has different expression
# in Andy's code, double check
# pn_beta, pn_sigma, pn_gamma = pycbc.pnutils.mass1_mass2_spin1z_spin2z_to_beta_sigma_gamma(
# mass1, mass2, chi*kappa, 0) # FIXME: spin2 is taken to be 0
pn_beta = (113. * mass1 / (12. * M) - 19. * eta / 6.) * chi * kappa
pn_sigma = (
(5. * (3. * kappa * kappa - 1.) / 2.) +
(7. - kappa * kappa) / 96.) * (mass1 * mass1 * chi * chi / M / M)
pn_gamma = (5. * (146597. + 7056. * eta) * mass1 /
(2268. * M) - 10. * eta *
(1276. + 153. * eta) / 81.) * chi * kappa
prec_fac0 = 5. * (4. + 3. * mass2 / mass1) / 64.
dtdv2 = 743. / 336. + 11. * eta / 4.
dtdv3 = -4. * lal.PI + pn_beta
dtdv4 = 3058673. / 1016064. + 5429. * eta / 1008. + 617. * eta * eta / 144. - pn_sigma
dtdv5 = (-7729. / 672. + 13. * eta / 8.) * lal.PI + 9. * pn_gamma / 40.
#####Calculate the Initial Euler Angles alpha_ref, beta_ref=0 and zeta_ref#####
gam = gamma0 * v0
sqrtfac = sqrt(1. + 2. * kappa * gam + gam * gam)
logv0 = log(v0)
logfac1 = log(1. + kappa * gam + sqrtfac)
logfac2 = log(kappa + gam + sqrtfac)
v02 = v0 * v0
v03 = v0 * v02
kappa2 = kappa * kappa
kappa3 = kappa2 * kappa
gamma02 = gamma0 * gamma0
gamma03 = gamma02 * gamma0
alpha_ref = prec_fac0 * (
logfac2 * (dtdv2 * gamma0 + dtdv3 * kappa - dtdv5 * kappa /
(2. * gamma02) + dtdv4 / (2. * gamma0) - dtdv4 * kappa2 /
(2. * gamma0) + (dtdv5 * kappa3) /
(2. * gamma02)) + logfac1 *
(-dtdv2 * gamma0 * kappa - dtdv3 + kappa * gamma03 / 2. -
gamma03 * kappa3 / 2.) + logv0 *
(dtdv2 * gamma0 * kappa + dtdv3 - kappa * gamma03 / 2. +
gamma03 * kappa3 / 2.) + sqrtfac *
(dtdv3 + dtdv4 * v0 / 2. + dtdv5 / gamma02 / 3. + dtdv4 * kappa /
(2. * gamma0) + dtdv5 * kappa * v0 / (6. * gamma0) - dtdv5 * kappa2 /
(2. * gamma02) - 1 / (3. * v03) - gamma0 * kappa /
(6. * v02) - dtdv2 / v0 - gamma02 / (3. * v0) + gamma02 * kappa2 /
(2. * v0) + dtdv5 * v02 / 3.)) - alpha0
zeta_ref = prec_fac0 * (
dtdv3 * gamma0 * kappa * v0 + dtdv4 * v0 + logfac2 *
(-dtdv2 * gamma0 - dtdv3 * kappa + dtdv5 * kappa /
(2. * gamma02) - dtdv4 / (2. * gamma0) + dtdv4 * kappa2 /
(2. * gamma0) - dtdv5 * kappa3 / (2. * gamma02)) + logv0 *
(kappa * gamma03 / 2. - gamma03 * kappa3 / 2.) + logfac1 *
(dtdv2 * gamma0 * kappa + dtdv3 - kappa * gamma03 / 2. +
gamma03 * kappa3 / 2.) - 1 / (3. * v03) - gamma0 * kappa /
(2. * v02) - dtdv2 / v0 + dtdv4 * gamma0 * kappa * v02 / 2. +
dtdv5 * v02 / 2. + sqrtfac *
(-dtdv3 - dtdv4 * v0 / 2. - dtdv5 / (3. * gamma02) - dtdv4 * kappa /
(2. * gamma0) - dtdv5 * kappa * v0 / (6. * gamma0) + dtdv5 * kappa2 /
(2. * gamma02) + 1 / (3. * v03) + gamma0 * kappa /
(6. * v02) + dtdv2 / v0 + gamma02 / (3. * v0) - gamma02 * kappa2 /
(2. * v0) - dtdv5 * v02 / 3.) + dtdv5 * gamma0 * kappa * v03 / 3.)
#####Calculate the Complex sideband factors, mm=2 is first entry#####
RE_SBfac0 = (1. + cos(thetaJ)**2) / 2.
RE_SBfac1 = sin(2. * thetaJ)
RE_SBfac2 = 3. * sin(thetaJ)**2
RE_SBfac3 = -sin(2. * thetaJ)
RE_SBfac4 = (1. + cos(thetaJ)**2) / 2.
IM_SBfac0 = -cos(thetaJ)
IM_SBfac1 = -2. * sin(thetaJ)
IM_SBfac2 = 0.
IM_SBfac3 = -2. * sin(thetaJ)
IM_SBfac4 = cos(thetaJ)
#####Calculate the PN terms # FIXME replace with functions in lalsimulation #####
theta = -11831. / 9240.
lambdaa = -1987. / 3080.0
pfaN = 3.0 / (128.0 * eta)
pfa2 = 5.0 * (743.0 / 84 + 11.0 * eta) / 9.0
pfa3 = -16.0 * lal.PI + 4.0 * pn_beta
pfa4 = 5.0*(3058.673/7.056 + 5429.0/7.0 * eta + 617.0 * eta*eta)/72.0 - \
10.0*pn_sigma
pfa5 = 5.0 / 9.0 * (7729.0 / 84.0 - 13.0 * eta) * lal.PI - pn_gamma
pfl5 = 5.0 / 3.0 * (7729.0 / 84.0 - 13.0 * eta) * lal.PI - pn_gamma * 3
pfa6 = (11583.231236531/4.694215680 - 640.0/3.0 * lal.PI * lal.PI- \
6848.0/21.0*lal.GAMMA) + \
eta * (-15335.597827/3.048192 + 2255./12. * lal.PI * \
lal.PI - 1760./3.*theta +12320./9.*lambdaa) + \
eta*eta * 76055.0/1728.0 - \
eta*eta*eta* 127825.0/1296.0
pfl6 = -6848.0 / 21.0
pfa7 = lal.PI * 5.0/756.0 * ( 15419335.0/336.0 + 75703.0/2.0 * eta - \
14809.0 * eta*eta)
FTaN = 32.0 * eta * eta / 5.0
FTa2 = -(12.47 / 3.36 + 3.5 / 1.2 * eta)
FTa3 = 4.0 * lal.PI
FTa4 = -(44.711 / 9.072 - 92.71 / 5.04 * eta - 6.5 / 1.8 * eta * eta)
FTa5 = -(81.91 / 6.72 + 58.3 / 2.4 * eta) * lal.PI
FTa6 = (664.3739519 / 6.9854400 + 16.0 / 3.0 * lal.PI * lal.PI -
17.12 / 1.05 * lal.GAMMA +
(4.1 / 4.8 * lal.PI * lal.PI - 134.543 / 7.776) * eta -
94.403 / 3.024 * eta * eta - 7.75 / 3.24 * eta * eta * eta)
FTl6 = -8.56 / 1.05
FTa7 = -(162.85/5.04 - 214.745/1.728 * eta - 193.385/3.024 * eta*eta) \
* lal.PI
dETaN = 2 * -eta / 2.0
dETa1 = 2 * -(3.0 / 4.0 + 1.0 / 12.0 * eta)
dETa2 = 3 * -(27.0 / 8.0 - 19.0 / 8.0 * eta + 1. / 24.0 * eta * eta)
dETa3 = 4 * -(67.5 / 6.4 -
(344.45 / 5.76 - 20.5 / 9.6 * lal.PI * lal.PI) * eta +
15.5 / 9.6 * eta * eta + 3.5 / 518.4 * eta * eta * eta)
amp0 = -4. * mass1 * mass2 / (1.0e+06 * distance * lal.PC_SI ) * \
lal.MRSUN_SI * lal.MTSUN_SI * sqrt(lal.PI/12.0)
htildeP = FrequencySeries(zeros(n, dtype=complex128),
delta_f=delta_f,
copy=False)
htildeC = FrequencySeries(zeros(n, dtype=complex128),
delta_f=delta_f,
copy=False)
spintaylorf2_kernel(htildeP.data[kmin:kmax], htildeC.data[kmin:kmax], kmin,
phase_order, amplitude_order, delta_f, piM, pfaN, pfa2,
pfa3, pfa4, pfa5, pfl5, pfa6, pfl6, pfa7, FTaN, FTa2,
FTa3, FTa4, FTa5, FTa6, FTl6, FTa7, dETaN, dETa1,
dETa2, dETa3, amp0, tC, phi0, kappa, prec_fac0,
alpha_ref, zeta_ref, dtdv2, dtdv3, dtdv4, dtdv5,
RE_SBfac0, RE_SBfac1, RE_SBfac2, RE_SBfac3, RE_SBfac4,
IM_SBfac0, IM_SBfac1, IM_SBfac2, IM_SBfac3, IM_SBfac4,
psiJ_P, psiJ_C, gamma0)
return htildeP, htildeC
def spa_tmplt(**kwds):
"""
"""
# Pull out the input arguments
f_lower = kwds['f_lower']
delta_f = kwds['delta_f']
distance = kwds['distance']
mass1 = kwds['mass1']
mass2 = kwds['mass2']
phase_order = int(kwds['phase_order'])
amplitude_order = int(kwds['amplitude_order'])
if 'out' in kwds:
out = kwds['out']
else:
out = None
tC = -1.0 / delta_f
amp_factor = spa_amplitude_factor(mass1=mass1, mass2=mass2) / distance
#Calculate the spin corrections
beta, sigma, gamma = pycbc.pnutils.mass1_mass2_spin1z_spin2z_to_beta_sigma_gamma(
mass1, mass2, kwds['spin1z'], kwds['spin2z'])
#Calculate the PN terms #TODO: replace with functions in lalsimulation!###
M = float(mass1) + float(mass2)
eta = mass1 * mass2 / (M * M)
theta = -11831./9240.;
lambdaa = -1987./3080.0;
pfaN = 3.0/(128.0 * eta);
pfa2 = 5*(743.0/84 + 11.0 * eta)/9.0;
pfa3 = -16.0*lal.PI + 4.0*beta;
pfa4 = 5.0*(3058.673/7.056 + 5429.0/7.0 * eta + 617.0 * eta*eta)/72.0 - \
10.0*sigma
pfa5 = 5.0/9.0 * (7729.0/84.0 - 13.0 * eta) * lal.PI - gamma
pfl5 = 5.0/3.0 * (7729.0/84.0 - 13.0 * eta) * lal.PI - gamma * 3
pfa6 = (11583.231236531/4.694215680 - 640.0/3.0 * lal.PI * lal.PI- \
6848.0/21.0*lal.GAMMA) + \
eta * (-15335.597827/3.048192 + 2255./12. * lal.PI * \
lal.PI - 1760./3.*theta +12320./9.*lambdaa) + \
eta*eta * 76055.0/1728.0 - \
eta*eta*eta* 127825.0/1296.0
pfl6 = -6848.0/21.0;
pfa7 = lal.PI * 5.0/756.0 * ( 15419335.0/336.0 + 75703.0/2.0 * eta - \
14809.0 * eta*eta)
m_sec = M * lal.MTSUN_SI;
piM = lal.PI * m_sec;
kmin = int(f_lower / float(delta_f))
vISCO = 1. / sqrt(6.)
fISCO = vISCO * vISCO * vISCO / piM;
kmax = int(fISCO / delta_f)
f_max = ceilpow2(fISCO);
n = int(f_max / delta_f) + 1;
if not out:
htilde = FrequencySeries(zeros(n, dtype=numpy.complex64), delta_f=delta_f, copy=False)
else:
if type(out) is not Array:
raise TypeError("Output must be an instance of Array")
if len(out) < kmax:
kmax = len(out)
if out.dtype != complex64:
raise TypeError("Output array is the wrong dtype")
htilde = FrequencySeries(out, delta_f=delta_f, copy=False)
spa_tmplt_engine(htilde[kmin:kmax], kmin, phase_order, delta_f, piM, pfaN,
pfa2, pfa3, pfa4, pfa5, pfl5,
pfa6, pfl6, pfa7, amp_factor)
return htilde