def myloglike_BaKa2016_EOSFit(cube, ndim, nparams):
t0 = cube[0]
m1 = cube[1]
c1 = cube[2]
m2 = cube[3]
c2 = cube[4]
zp = cube[5]
mb1 = lightcurve_utils.EOSfit(m1,c1)
mb2 = lightcurve_utils.EOSfit(m2,c2)
tmag, lbol, mag = BaKa2016_model(m1,mb1,c1,m2,mb2,c2)
prob = calc_prob(tmag, lbol, mag, t0, zp)
prior = prior_DiUj2017(m1,mb1,c1,m2,mb2,c2)
if prior == 0.0:
prob = -np.inf
return prob
def myloglike_SmCh2017_EOSFit(cube, ndim, nparams):
t0 = cube[0]
m1 = cube[1]
c1 = cube[2]
m2 = cube[3]
c2 = cube[4]
slope_r = cube[5]
kappa_r = 10**cube[6]
zp = cube[7]
mb1 = lightcurve_utils.EOSfit(m1,c1)
mb2 = lightcurve_utils.EOSfit(m2,c2)
tmag, lbol, mag = SmCh2017_model(m1,mb1,c1,m2,mb2,c2,slope_r,kappa_r)
prob = calc_prob(tmag, lbol, mag, t0, zp)
prior = prior_DiUj2017(m1,mb1,c1,m2,mb2,c2)
if prior == 0.0:
prob = -np.inf
return prob
def myloglike_KaKy2016_EOSFit(cube, ndim, nparams):
t0 = cube[0]
q = cube[1]
chi_eff = cube[2]
mns = cube[3]
c = cube[4]
th = cube[5]
ph = cube[6]
zp = cube[7]
mb = lightcurve_utils.EOSfit(mns,c)
tmag, lbol, mag = KaKy2016_model(q, chi_eff, mns, mb, c, th, ph)
prob = calc_prob(tmag, lbol, mag, t0, zp)
prior = prior_KaKy2016(q,chi_eff,mns,mb,c)
if prior == 0.0:
prob = -np.inf
return prob
snr = data_out[:, 1]
m1 = data_out[:, 2]
m2 = data_out[:, 3]
q = 1 / data_out[:, 4]
eff_spin = data_out[:, 5]
totmass = data_out[:, 6]
a1 = data_out[:, 7]
a2 = data_out[:, 8]
for ii in xrange(len(injnum)):
if not ((q[ii] >= 3) and (q[ii] <= 9)): continue
if not ((m2[ii] >= 1) and (m2[ii] <= 3)): continue
#if not ((q[ii] >= 3) and (q[ii] <=5)): continue
c1, c2 = 0.147, 0.147
mb1, mb2 = lightcurve_utils.EOSfit(m1[ii],
c1), lightcurve_utils.EOSfit(
m2[ii], c2)
th = 0.2
ph = 3.14
t, lbol, mag = KaKy2016_model(q[ii], a1[ii], m2[ii], mb2, c2, th, ph)
#print q[ii],a1[ii],m2[ii],mb2,c2,th,ph
if len(mag) == 0: continue
idx = np.where(np.isfinite(mag[2]))[0]
if opts.doEOSFit:
eosfitFlag = "--doEOSFit"
else:
eosfitFlag = ""
if opts.doEjecta:
eta = lightcurve_utils.q2eta(np.mean(data_out["q"]))
m1, m2 = lightcurve_utils.mc2ms(np.mean(data_out["mc"]), eta)
mchirp, eta, q = lightcurve_utils.ms2mc(m1, m2)
q = 1 / q
else:
eta = lightcurve_utils.q2eta(data_out["q"])
m1, m2 = lightcurve_utils.mc2ms(data_out["mc"], eta)
q = m2 / m1
mc = data_out["mc"]
m1, m2 = np.mean(m1), np.mean(m2)
chi_eff = 0.0
c1, c2 = 0.147, 0.147
mb1, mb2 = lightcurve_utils.EOSfit(m1, c1), lightcurve_utils.EOSfit(
m2, c2)
th = 0.2
ph = 3.14
if m1 > 3:
from gwemlightcurves.EjectaFits.KaKy2016 import calc_meje, calc_vave
mej = calc_meje(q, chi_eff, c2, mb2, m2)
vej = calc_vave(q)
else:
from gwemlightcurves.EjectaFits.DiUj2017 import calc_meje, calc_vej
mej = calc_meje(m1, mb1, c1, m2, mb2, c2)
vej = calc_vej(m1, c1, m2, c2)
filename = os.path.join(plotDir, 'truth_mej_vej.dat')
q_true = 1/q_true
multifile = lightcurve_utils.get_post_file(plotDir)
if not multifile: continue
data = np.loadtxt(multifile)
post[name][errorbudget] = {}
if opts.doGoingTheDistance or opts.doMassGap:
if opts.model == "DiUj2017":
if opts.doEOSFit:
mchirp_gw,eta_gw,q_gw = lightcurve_utils.ms2mc(data[:,0],data[:,2])
mej_gw, vej_gw = np.zeros(data[:,0].shape), np.zeros(data[:,0].shape)
ii = 0
for m1,c1,m2,c2 in data[:,:-1]:
mb1 = lightcurve_utils.EOSfit(m1,c1)
mb2 = lightcurve_utils.EOSfit(m2,c2)
mej_gw[ii], vej_gw[ii] = bns_model(m1,mb1,c1,m2,mb2,c2)
ii = ii + 1
else:
mchirp_gw,eta_gw,q_gw = lightcurve_utils.ms2mc(data[:,0],data[:,3])
mej_gw, vej_gw = np.zeros(data[:,0].shape), np.zeros(data[:,0].shape)
ii = 0
for m1,mb1,c1,m2,mb2,c2 in data[:,:-1]:
mej_gw[ii], vej_gw[ii] = bns_model(m1,mb1,c1,m2,mb2,c2)
ii = ii + 1
q_gw = 1/q_gw
mej_gw = np.log10(mej_gw)
elif opts.model == "KaKy2016":
if opts.doEOSFit:
mchirp_gw,eta_gw,q_gw = lightcurve_utils.ms2mc(data[:,0]*data[:,2],data[:,2])
