def get_KaKy2016_model(table, **kwargs):
if not 'mej' in table.colnames:
# calc the mass of ejecta
table['mej'] = calc_meje(table['q'], table['chi_eff'], table['c'], table['mb'], table['mns'])
# calc the velocity of ejecta
table['vej'] = calc_vave(table['q'])
# Throw out smaples where the mass ejecta is less than zero.
mask = (table['mej'] > 0)
table = table[mask]
if len(table) == 0: return table
# Log mass ejecta
table['mej10'] = np.log10(table['mej'])
# Initialize columns
timeseries = np.arange(table['tini'][0], table['tmax'][0]+table['dt'][0], table['dt'][0])
table['t'] = [np.zeros(timeseries.size)]
table['lbol'] = [np.zeros(timeseries.size)]
table['mag'] = [{}]
# Loop over samples
for isample in range(len(table)):
table['t'][isample], table['lbol'][isample], table['mag'][isample] = calc_lc(table['tini'][isample], table['tmax'][isample], table['dt'][isample],
table['mej'][isample], table['vej'][isample], table['vmin'][isample],
table['th'][isample], table['ph'][isample], table['kappa'][isample],
table['eps'][isample], table['alp'][isample], table['eth'][isample])
return table
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')
fid = open(filename, 'w+')
fid.write('%.5f %.5f\n' % (mej, vej))
fid.close()
if m1 > 3:
filename = os.path.join(plotDir, 'truth.dat')
fid = open(filename, 'w+')
