def k2lc(epic):
"""
load k2 light curve
"""
prefix = epic[:4]
id = epic[4:]
c = "01"
path = "data/c01/{0}00000/{1}".format(prefix, id)
end = "kepler_v1.0_lc.fits"
file = "{0}/hlsp_everest_k2_llc_{1}-c{2}_{3}".format(path, epic, c, end)
x, y = process_data(file)
return x, y
# save samples
f = h5py.File("%s_samples.h5" % id, "w")
data = f.create_dataset("samples", np.shape(sampler.chain))
data[:, :] = np.array(sampler.chain)
f.close()
# make various plots
if plot:
with h5py.File("%s_samples.h5" % id, "r") as f:
samples = f["samples"][...]
mcmc_result = make_plot(samples, xsub, ysub, yerrsub, id, fn,
traces=True, tri=True, prediction=True)
if __name__ == "__main__":
c = "01"
epic = "201131793"
path = "data/c01/201100000/31793"
end = "kepler_v1.0_lc.fits"
file = "{0}/hlsp_everest_k2_llc_{1}-c{2}_{3}".format(path, epic, c, end)
if os.path.exists(file):
x, y = process_data(file)
burnin, run, npts, tol = 1000, 100000, 10, .4 # MCMC. max npts is 48
yerr = np.ones_like(y) * 1e-5
interval = 0.02043365 # assume for long cadence
recover_injections(id, x, y, yerr, path, burnin, run, interval, tol,
npts, nwalkers=12, plot=True)
else:
print(file, "file not found")
