labels.append('$e$')
# add omega to the list
omega = np.arctan2(x[:, 3], x[:, 2]).reshape((len(x[:, 0]), 1))*180./np.pi
x = np.concatenate((x, omega), axis=1)
labels.append('$\omega$ (deg)')
# if we want to get inferred value from the isochrones as well
if inferredparams:
# some important values
FeH = x[:, 8]
# convert to log(age) for the isochrone
age = np.log10(x[:, 9] * 1e9)
M = x[:, 7]
# set up the output
results = np.zeros((len(FeH), len(isointerp(M[0], FeH[0],
age[0], isobundle))))
# get the isochrone values for each chain input
# this is very time intensive
for ii in np.arange(len(FeH)):
results[ii, :] = isointerp(M[ii], FeH[ii], age[ii], isobundle)
# add primary effective temperature
Teff = (10.**results[:, -1]).reshape((len(x[:, 0]), 1))
x = np.concatenate((x, Teff), axis=1)
labels.append('$T_{eff,1}$ (K)')
# add log(g)
logg = (results[:, -2]).reshape((len(x[:, 0]), 1))
x = np.concatenate((x, logg), axis=1)
labels.append('log(g)')
# add primary radius
# ========================================================================== #
# the rest is all to produce Figure S4
# most of this is copied from the light_curve_model function
# set up a range of WD masses
M2s = np.linspace(0.1, 1.453, 1000)
(magobs, magerr, maglam, magname, interps, limits, fehs, ages,
maxmasses, wdmagfunc) = isobundle
# calculate their magnitudes based on the best model
wdage = np.log10(10.**age - 10.**(msage(M2init, FeH, isobundle)))
wdmag = wdmagfunc(np.array([[M2, wdage]]))[0]
# get the magnitudes of the primary star in the best model
mags = isointerp(M1, FeH, age, isobundle)
# pull out the stellar radius
R1 = mags[-3]
# get the flux ratio between the two stars in the Kepler band
F2F1 = 0.
if np.isfinite(wdmag):
# get the Kp magnitude of the main star
gind = np.where(magname == 'g')[0][0]
rind = np.where(magname == 'r')[0][0]
iind = np.where(magname == 'i')[0][0]
if mags[gind] - mags[rind] <= 0.3:
kpmag1 = 0.25 * mags[gind] + 0.75 * mags[rind]
else:
kpmag1 = 0.3 * mags[gind] + 0.7 * mags[iind]
