def plotprior(ax, lc, normalize=False, **kwargs):
P = utils.periodprior(lc.t, lc.flux)
x = np.linspace(0, max(lc.t) - min(lc.t), 1000)
y = np.exp(P.logp(x).eval())
if normalize:
y = y / np.max(y)
return ax.plot(x, y, **kwargs)
cornerfile = kid + "_corner.png"
errfile = kid + "_err.dat"
lc = Lightcurve.everest(f)
try:
y = lc.flux - utils.trend(lc.t, lc.flux, 2)
mask = sigma_clip(y-medfilt(y, kernel_size=5), sigma=3)
x = lc.t[mask.mask == False]
y = y[mask.mask == False]
yerr = np.std(y - medfilt(y, kernel_size=5))
with pm.Model() as model:
mean = pm.Normal("mean", mu=np.mean(y), sd=np.std(y))
yerr = pm.Normal("yerr", mu=yerr, sd=5.0)
logamp = pm.Normal("logamp", mu=np.log(np.var(y)), sd=5)
period = utils.periodprior(lc.t, lc.flux)
logQ0 = pm.Uniform("logQ0", lower=-10, upper=10)
logdQ = pm.Normal("logdQ", mu=2.0, sd=5.0)
mix = pm.Uniform("mix", lower=0.0, upper=1.0)
logS0 = pm.Normal("logS0", mu=np.log(np.var(y)), sd=5)
dt = lc.t[-1] - lc.t[0]
logw = pm.Uniform("logw", lower=-20, upper=0)
kernel = xo.gp.terms.RotationTerm(
log_amp=logamp,
period=period,
log_Q0=logQ0,
log_deltaQ=logdQ,
mix=mix
)
lc = Lightcurve.tess(f)
try:
y = lc.flux - utils.trend(lc.t, lc.flux, 2)
mask = sigma_clip(y - medfilt(y, kernel_size=301), sigma=3)
x = lc.t[mask.mask == False]
y = y[mask.mask == False]
yerr = np.std(y - medfilt(y, kernel_size=51))
x = x[::5]
y = y[::5]
with pm.Model() as model:
mean = pm.Normal("mean", mu=np.mean(y), sd=np.std(y))
yerr = pm.Normal("yerr", mu=yerr, sd=5.0)
logamp = pm.Normal("logamp", mu=np.log(np.var(y)), sd=5)
period = utils.periodprior(lc.t, lc.flux, min_period=0.1)
logQ0 = pm.Uniform("logQ0", lower=-10, upper=10)
logdQ = pm.Normal("logdQ", mu=2.0, sd=5.0)
mix = pm.Uniform("mix", lower=0.0, upper=1.0)
logS0 = pm.Normal("logS0", mu=np.log(np.var(y)), sd=5)
dt = lc.t[-1] - lc.t[0]
logw = pm.Uniform("logw", lower=-20, upper=0)
kernel = xo.gp.terms.RotationTerm(log_amp=logamp,
period=period,
log_Q0=logQ0,
log_deltaQ=logdQ,
mix=mix)
kernel += xo.gp.terms.SHOTerm(log_S0=logS0,
log_w0=logw,