cs[:, -1] = 1 - 0.9 * np.array(b2) / np.max(b2)
for order, i in enumerate(np.argsort(np.sqrt(e2) *
np.array(p.mean_flux))[::-1]):
pl.clf()
star = Star(_id=p.stars[i])
ra, dec = star.ra, star.dec
tai, flux, ferr = p.get_lightcurve(p.stars[i])
# Convert to MJD.
mjd = tai / 24 / 3600
T = lyrae.find_period({"g": mjd}, {"g": flux}, {"g": ferr})
lcmodel = lyrae.get_model(T, mjd, flux, ferr)
print "Period: ", T, "chi2: ", lcmodel[-1]
# Plot the light curve with errorbars and alpha weight
# based on badness of the run.
pl.errorbar(mjd % T, flux, yerr=ferr, ls="None", capsize=0, lw=2,
marker="o", zorder=1, barsabove=False, color="k")
pl.errorbar(mjd % T + T, flux, yerr=ferr, ls="None", capsize=0,
lw=2, marker="o", zorder=1, barsabove=False, color="k")
# Plot the fit.
t = np.linspace(0, 2 * T, 5000)
pl.plot(t, lcmodel[0](t), "--k")
# Plot the fit stellar flux.
pl.gca().axhline(p.mean_flux[i], color="k")
for ind in [144]: # range(len(sesar.table2)):
for order in [12]: # [3, 6, 9, 12]:
_id = str(sesar.table2[ind]["Num"])
print "%d:" % ind, _id, order
d = sesar.table1[_id][...]
bands = "gri"
inds = dict([(b, (d[b] > 1) * (d[b] < 99)) for b in bands])
time = dict([(b, d[b + "mjd"][inds[b]]) for b in bands])
flux = dict([(b, 10 ** (9 - 0.4 * d[b][inds[b]])) for b in bands])
ferr = dict([(b, flux[b] * np.log(10) * 0.4 * d[b + "err"][inds[b]]) for b in bands])
p = [sesar.table2[ind]["Per"], lyrae.find_period(time, flux, ferr, order=order)]
m = dict([(b, [lyrae.get_model(p0, time[b], flux[b], ferr[b], order=order) for p0 in p]) for b in time])
f = open(fn, "a")
s = ["%4d" % ind, _id]
s += ["%.8f" % p[1]]
s += [str(order)]
s += ["%.0f" % m[b][1][2] for b in time]
for b in m:
a = m[b][1][1]
s += ["%e" % a0 for a0 in a]
f.write(" ".join(s) + "\n")
f.close()
def plot_lc(b, i):
N = 5413
t = np.linspace(min(time[b]), max(time[b]), N)
