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 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 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 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 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
fignum = fignum + 1
pylab.figure(fignum)
pylab.semilogy([f.mass1 + f.mass2 for f in found],
[f.eff_dist_l for f in found], '.')
pylab.semilogy([m.mass1 + m.mass2 for m in missed],
[m.eff_dist_l for m in missed], 'k.')
pylab.xlabel('Total Mass ($M_\odot$)')
pylab.ylabel('Effective Distance (Mpc): L1')
pylab.title('Missed-Found:L1 Effective Distance vs Total Mass')
pylab.savefig('L1effdist_mass.png')
fignum = fignum + 1
pylab.figure(fignum)
pylab.semilogy([
spawaveform.computechi(f.mass1, f.mass2, f.spin1z, f.spin2z)
for f in found
], [f.eff_dist_l for f in found], '.')
pylab.semilogy([
spawaveform.computechi(m.mass1, m.mass2, m.spin1z, f.spin2z)
for m in missed
], [m.eff_dist_l for m in missed], 'k.')
pylab.xlabel('Mass Weighted Spin')
pylab.ylabel('Effective Distance (Mpc): L1')
pylab.title('Missed-Found:L1 Effective Distance vs Mass Weighted Spin')
pylab.savefig('L1effdist_massweightedspin.png')
fignum = fignum + 1
pylab.figure(fignum)
pylab.semilogy(
[finalspin(f.mass1, f.mass2, f.spin1z, f.spin2z) for f in found],
else: theta1L[s]=sim['th1L']
if np.isnan(sim['th2L']): theta2L[s]=0.0
else: theta2L[s]=sim['th2L']
if np.isnan(sim['thSL']): theta1L[s]=0.0
else: thetaSL[s]=sim['thSL']
for n in xrange(nsamples):
mass1, mass2 = bwave.component_masses(total_masses[s, n], mass_ratios[s])
chirp_masses[s, n] = spawaveform.chirpmass(mass1, mass2) \
/ lal.MTSUN_SI
chis[s, n] = spawaveform.computechi(mass1, mass2, spin1z, spin2z)
median_mchirps = np.median(chirp_masses, axis=1)
std_mchirps = np.std(chirp_masses, axis=1)
median_chis = np.median(chis, axis=1)
std_chis = np.std(chis, axis=1)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# SCATTER PLOTS
# --- Mass vs thSL Scatter plot
f, ax = pl.subplots()
err = ax.errorbar(median_total_masses, thetaSL, xerr=std_total_masses, color='k',
pylab.ylabel('Effective Distance (Mpc): L1')
pylab.title('Missed-Found:L1 Effective Distance vs Chirp Mass')
pylab.savefig('L1effdist_chirpmass.png')
fignum = fignum + 1
pylab.figure(fignum)
pylab.semilogy([f.mass1+f.mass2 for f in found], [f.eff_dist_l for f in found],'.')
pylab.semilogy([m.mass1+m.mass2 for m in missed], [m.eff_dist_l for m in missed],'k.')
pylab.xlabel('Total Mass ($M_\odot$)')
pylab.ylabel('Effective Distance (Mpc): L1')
pylab.title('Missed-Found:L1 Effective Distance vs Total Mass')
pylab.savefig('L1effdist_mass.png')
fignum = fignum + 1
pylab.figure(fignum)
pylab.semilogy([spawaveform.computechi(f.mass1, f.mass2, f.spin1z, f.spin2z) for f in found], [f.eff_dist_l for f in found],'.')
pylab.semilogy([spawaveform.computechi(m.mass1, m.mass2, m.spin1z, f.spin2z) for m in missed], [m.eff_dist_l for m in missed],'k.')
pylab.xlabel('Mass Weighted Spin')
pylab.ylabel('Effective Distance (Mpc): L1')
pylab.title('Missed-Found:L1 Effective Distance vs Mass Weighted Spin')
pylab.savefig('L1effdist_massweightedspin.png')
fignum = fignum + 1
pylab.figure(fignum)
pylab.semilogy([finalspin(f.mass1, f.mass2, f.spin1z, f.spin2z) for f in found], [f.eff_dist_l for f in found],'.')
pylab.semilogy([finalspin(m.mass1, m.mass2, m.spin1z, f.spin2z) for m in missed], [m.eff_dist_l for m in missed],'k.')
pylab.xlabel('Final Spin')
pylab.ylabel('Effective Distance (Mpc): L1')
pylab.title('Missed-Found:L1 Effective Distance vs Final Spin')
pylab.savefig('L1effdist_finalspin.png')
hplus_NR.data = bwave.window_wave(hplus_NR.data)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# --- Generate approximant with this NR waveform's parameters ---
#
mass_ratios[s] = simulation['q']
spin_magnitudes.append((simulation['a1'], simulation['a2']))
spin_angles.append((simulation['th1L'], simulation['th2L']))
mass1, mass2 = component_masses(total_mass, mass_ratios[s])
spin1z = cartesian_spins(simulation['a1'], simulation['th1L'])
spin2z = cartesian_spins(simulation['a2'], simulation['th2L'])
effective_spins[s] = spawaveform.computechi(mass1, mass2, spin1z, spin2z)
print "Generating EOBNR"
hplus_EOBNR, _ = get_td_waveform(approximant="SEOBNRv2",
distance=distance,
mass1=mass1,
mass2=mass2,
spin1z=spin1z,
spin2z=spin2z,
f_lower=f_low,
delta_t=SI_deltaT)
# divide out the spherical harmonic (2,2) amplitude (this is just for nice
# plots / sanity - it does not affect match)
sY22 = lal.SpinWeightedSphericalHarmonic(0,0,-2,2,2)
