def IMRtargetburstfreq(m1,m2,spin1z,spin2z):
"""
IMRtargetburstfreq finds the peak amplitude of the waveform for a given source parameters and the source distance.
usage: IMRtargetburstfreq(m1,m2,spin1z,spin2z)
e.g.
spawaveApp.IMRtargetburstfreq(30,40,0.45,0.5)
"""
chi = spawaveform.computechi(m1, m2, spin1z, spin2z)
fFinal = spawaveform.ffinal(m1,m2,'schwarz_isco')
imrfFinal = spawaveform.imrffinal(m1, m2, chi, 'fcut')
fLower = 40.0
order = 7
dur = 2**numpy.ceil(numpy.log2(spawaveform.chirptime(m1,m2,order,fFinal)))
sr = 2**numpy.ceil(numpy.log2(imrfFinal*2))
deltaF = 1.0 / dur
deltaT = 1.0 / sr
s = numpy.empty(sr * dur, 'complex128')
spawaveform.imrwaveform(m1, m2, deltaF, fFinal, s, spin1z, spin2z)
#S = numpy.real(s)
S = numpy.abs(s)
x = scipy.linspace(fFinal, imrfFinal, numpy.size(S))
N = interpolatation(x)
#N = interpolate.splev(x,interpolatation)
ratio = numpy.divide(numpy.square(S),numpy.square(N))
#ratio = numpy.divide(S,N)
maxindex = numpy.argmax(ratio)
maxfreq = x[maxindex]
return maxfreq
def IMRsnr(m1,m2,spin1z,spin2z,d):
"""
IMRsnr finds the SNR of the waveform with Initial LIGO SRD for a given source parameters and the source distance.
usage: IMRsnr(m1,m2,spin1z,spin2z,distance)
e.g.
spawaveApp.IMRsnr(30,40,0.45,0.5,100)
"""
chi = spawaveform.computechi(m1, m2, spin1z, spin2z)
imrfFinal = spawaveform.imrffinal(m1, m2, chi, 'fcut')
fLower = 10.0
order = 7
dur = 2**numpy.ceil(numpy.log2(spawaveform.chirptime(m1,m2,order,fLower)))
sr = 2**numpy.ceil(numpy.log2(imrfFinal*2))
deltaF = 1.0 / dur
deltaT = 1.0 / sr
s = numpy.empty(sr * dur, 'complex128')
spawaveform.imrwaveform(m1, m2, deltaF, fLower, s, spin1z, spin2z)
S = numpy.abs(s)
x = scipy.linspace(fLower, imrfFinal, numpy.size(S))
N = interpolatation(x)
#N = interpolate.splev(x,interpolatation)
SNR = math.sqrt(numpy.sum(numpy.divide(numpy.square(S),numpy.square(N))))/d
return SNR
def IMRsnr(m1, m2, spin1z, spin2z, d):
"""
IMRsnr finds the SNR of the waveform with Initial LIGO SRD for a given source parameters and the source distance.
usage: IMRsnr(m1,m2,spin1z,spin2z,distance)
e.g.
spawaveApp.IMRsnr(30,40,0.45,0.5,100)
"""
chi = spawaveform.computechi(m1, m2, spin1z, spin2z)
imrfFinal = spawaveform.imrffinal(m1, m2, chi, 'fcut')
fLower = 10.0
order = 7
dur = 2**numpy.ceil(
numpy.log2(spawaveform.chirptime(m1, m2, order, fLower)))
sr = 2**numpy.ceil(numpy.log2(imrfFinal * 2))
deltaF = 1.0 / dur
deltaT = 1.0 / sr
s = numpy.empty(sr * dur, 'complex128')
spawaveform.imrwaveform(m1, m2, deltaF, fLower, s, spin1z, spin2z)
S = numpy.abs(s)
x = scipy.linspace(fLower, imrfFinal, numpy.size(S))
N = interpolatation(x)
#N = interpolate.splev(x,interpolatation)
SNR = math.sqrt(numpy.sum(numpy.divide(numpy.square(S),
numpy.square(N)))) / d
return SNR
def IMRpeakAmp(m1,m2,spin1z,spin2z,d):
"""
IMRpeakAmp finds the peak amplitude of the waveform for a given source parameters and the source distance.
usage: IMRpeakAmp(m1,m2,spin1z,spin2z,distance)
e.g.
spawaveApp.IMRpeakAmp(30,40,0.45,0.5,100)
"""
chi = spawaveform.computechi(m1, m2, spin1z, spin2z)
imrfFinal = spawaveform.imrffinal(m1, m2, chi, 'fcut')
fLower = 10.0
order = 7
dur = 2**numpy.ceil(numpy.log2(spawaveform.chirptime(m1,m2,order,fLower)))
sr = 2**numpy.ceil(numpy.log2(imrfFinal*2))
deltaF = 1.0 / dur
deltaT = 1.0 / sr
s = numpy.empty(sr * dur, 'complex128')
spawaveform.imrwaveform(m1, m2, deltaF, fLower, s, spin1z, spin2z)
s = scipy.ifft(s)
#s = numpy.abs(s)
s = numpy.real(s)
max = numpy.max(s)/d
return max
def IMRtargetburstfreq(m1, m2, spin1z, spin2z):
"""
IMRtargetburstfreq finds the peak amplitude of the waveform for a given source parameters and the source distance.
usage: IMRtargetburstfreq(m1,m2,spin1z,spin2z)
e.g.
spawaveApp.IMRtargetburstfreq(30,40,0.45,0.5)
"""
chi = spawaveform.computechi(m1, m2, spin1z, spin2z)
fFinal = spawaveform.ffinal(m1, m2, 'schwarz_isco')
imrfFinal = spawaveform.imrffinal(m1, m2, chi, 'fcut')
fLower = 40.0
order = 7
dur = 2**numpy.ceil(
numpy.log2(spawaveform.chirptime(m1, m2, order, fFinal)))
sr = 2**numpy.ceil(numpy.log2(imrfFinal * 2))
deltaF = 1.0 / dur
deltaT = 1.0 / sr
s = numpy.empty(sr * dur, 'complex128')
spawaveform.imrwaveform(m1, m2, deltaF, fFinal, s, spin1z, spin2z)
#S = numpy.real(s)
S = numpy.abs(s)
x = scipy.linspace(fFinal, imrfFinal, numpy.size(S))
N = interpolatation(x)
#N = interpolate.splev(x,interpolatation)
ratio = numpy.divide(numpy.square(S), numpy.square(N))
#ratio = numpy.divide(S,N)
maxindex = numpy.argmax(ratio)
maxfreq = x[maxindex]
return maxfreq
def IMRpeakAmp(m1, m2, spin1z, spin2z, d):
"""
IMRpeakAmp finds the peak amplitude of the waveform for a given source parameters and the source distance.
usage: IMRpeakAmp(m1,m2,spin1z,spin2z,distance)
e.g.
spawaveApp.IMRpeakAmp(30,40,0.45,0.5,100)
"""
chi = spawaveform.computechi(m1, m2, spin1z, spin2z)
imrfFinal = spawaveform.imrffinal(m1, m2, chi, 'fcut')
fLower = 10.0
order = 7
dur = 2**numpy.ceil(
numpy.log2(spawaveform.chirptime(m1, m2, order, fLower)))
sr = 2**numpy.ceil(numpy.log2(imrfFinal * 2))
deltaF = 1.0 / dur
deltaT = 1.0 / sr
s = numpy.empty(sr * dur, 'complex128')
spawaveform.imrwaveform(m1, m2, deltaF, fLower, s, spin1z, spin2z)
s = scipy.ifft(s)
#s = numpy.abs(s)
s = numpy.real(s)
max = numpy.max(s) / d
return max
def __init__(self, m1, m2, spin1x, spin1y, spin1z, spin2x, spin2y, spin2z, theta, phi, iota, psi, bank):
PrecessingTemplate.__init__(self, m1, m2, spin1x, spin1y, spin1z, spin2x, spin2y, spin2z, theta, phi, iota, psi, bank)
# derived quantities
self.chieff, self.chipre = SimIMRPhenomPCalculateModelParameters(self.m1, self.m2, self.bank.flow, np.sin(self.iota), float(0), np.cos(self.iota), float(self.spin1x), float(self.spin1y), float(self.spin1z), float(self.spin2x), float(self.spin2y), float(self.spin2z))[:2] # FIXME are the other four parameters informative?
self._f_final = spawaveform.imrffinal(m1, m2, self.chieff)
self._dur = self._imrdur()
def __init__(self, m1, m2, bank):
Template.__init__(self, m1, m2, bank)
self.m1 = float(m1)
self.m2 = float(m2)
self.bank = bank
# derived quantities
# we'll just use the imrphenomb ffinal estimate for f_final
self._f_final = spawaveform.imrffinal(m1, m2, 0)
self._dur = self._imrdur()
self._mchirp = compute_mchirp(m1, m2)
def __init__(self, m1, m2, spin1z, spin2z, bank):
Template.__init__(self, m1, m2, bank)
self.m1 = m1
self.m2 = m2
self.spin1z = spin1z
self.spin2z = spin2z
chi = lalsim.SimIMRPhenomBComputeChi( self.m1, self.m2, self.spin1z, self.spin2z )
self.chi = chi
self.bank = bank
# derived quantities
self._f_final = spawaveform.imrffinal(m1, m2, chi)
self._dur = self._imrdur()
self._mchirp = compute_mchirp(m1, m2)
def __init__(self, m1, m2, spin1z, spin2z, bank):
Template.__init__(self)
self.m1 = float(m1)
self.m2 = float(m2)
self.spin1z = float(spin1z)
self.spin2z = float(spin2z)
self.bank = bank
# derived quantities
self._chi = lalsim.SimIMRPhenomBComputeChi(m1, m2, spin1z, spin2z)
# we'll just use the imrphenomb ffinal estimate for f_final
self._f_final = spawaveform.imrffinal(m1, m2, self._chi)
self._dur = self._imrdur()
self._mchirp = compute_mchirp(m1, m2)
def IMRhrss(m1,m2,spin1z,spin2z,d):
"""
IMRhrss finds the SNR of the waveform for a given source parameters and the source distance.
usage: IMRhrss(m1,m2,spin1z,spin2z,distance)
e.g.
spawaveApp.IMRhrss(30,40,0.45,0.5,100)
"""
chi = spawaveform.computechi(m1, m2, spin1z, spin2z)
imrfFinal = spawaveform.imrffinal(m1, m2, chi, 'fcut')
fLower = 10.0
order = 7
dur = 2**numpy.ceil(numpy.log2(spawaveform.chirptime(m1,m2,order,fLower)))
sr = 2**numpy.ceil(numpy.log2(imrfFinal*2))
deltaF = 1.0 / dur
deltaT = 1.0 / sr
s = numpy.empty(sr * dur, 'complex128')
spawaveform.imrwaveform(m1, m2, deltaF, fLower, s, spin1z, spin2z)
s = numpy.abs(s)
s = numpy.square(s)
hrss = math.sqrt(numpy.sum(s))/d
return hrss
def IMRhrss(m1, m2, spin1z, spin2z, d):
"""
IMRhrss finds the SNR of the waveform for a given source parameters and the source distance.
usage: IMRhrss(m1,m2,spin1z,spin2z,distance)
e.g.
spawaveApp.IMRhrss(30,40,0.45,0.5,100)
"""
chi = spawaveform.computechi(m1, m2, spin1z, spin2z)
imrfFinal = spawaveform.imrffinal(m1, m2, chi, 'fcut')
fLower = 10.0
order = 7
dur = 2**numpy.ceil(
numpy.log2(spawaveform.chirptime(m1, m2, order, fLower)))
sr = 2**numpy.ceil(numpy.log2(imrfFinal * 2))
deltaF = 1.0 / dur
deltaT = 1.0 / sr
s = numpy.empty(sr * dur, 'complex128')
spawaveform.imrwaveform(m1, m2, deltaF, fLower, s, spin1z, spin2z)
s = numpy.abs(s)
s = numpy.square(s)
hrss = math.sqrt(numpy.sum(s)) / d
return hrss
def _get_f_final(self):
return spawaveform.imrffinal(self.m1, self.m2, self.chieff)
# --- Scale the NR waveform at this mass
# Scale the NR waveform to the mass we want
hplus_NR_new = pycbc.types.TimeSeries(bnru.scale_wave(hplus_NR, mass,
init_total_mass), delta_t=deltaT)
hplus_NR_new.data[:] = bnru.taper(hplus_NR_new.data[:],
delta_t=hplus_NR_new.delta_t)
# --- Generate the SEOBNR waveform to this mass
mass1, mass2 = bwave.component_masses(mass, simulations.simulations[w]['q'])
# Estimate ffinal
chi = spawaveform.computechi(mass1, mass2, spin1z, spin2z)
ffinal = spawaveform.imrffinal(mass1, mass2, chi)
Hf = hplus_NR_new.to_frequencyseries()
f_lower = 0.8*Hf.sample_frequencies.data[ np.argmax(abs(Hf)) ]
hplus_SEOBNR, _ = get_td_waveform(approximant="SEOBNRv2",
distance=distance,
mass1=mass1,
mass2=mass2,
spin1z=spin1z,
spin2z=spin2z,
f_lower=f_lower,
delta_t=deltaT)
hplus_SEOBNR.data = bnru.taper(hplus_SEOBNR.data,
delta_t=hplus_SEOBNR.delta_t)
def _get_f_final(self):
return spawaveform.imrffinal(self.m1, self.m2, self.chieff)
