lc.subtract_add_divide_without_outliers(params=hat11_params,
quarterly_max=quarterly_maxes[lc.quarters[0]],
plots=False)
lc_output_path = os.path.join(output_dir,
'lc{0:03d}.txt'.format(transit_number))
np.savetxt(lc_output_path, np.vstack([lc.times.jd, lc.fluxes, lc.errors]).T)
# Delete sharp outliers prior to peak-finding
lc.delete_outliers()
transit_model = generate_lc_depth(lc.times_jd, hat11_params.rp**2, hat11_params)
residuals = lc.fluxes - transit_model
# Find peaks in the light curve residuals
best_fit_spot_params = peak_finder(lc.times.jd, residuals, lc.errors,
hat11_params, n_peaks=4, plots=False,
verbose=True)
best_fit_gaussian_model = summed_gaussians(lc.times.jd,
best_fit_spot_params)
# If spots are detected:
if best_fit_spot_params is not None:
output_path = os.path.join(output_dir,
'chains{0:03d}.hdf5'.format(transit_number))
sampler = run_emcee_seeded(lc, hat11_params, best_fit_spot_params,
n_steps=15000, n_walkers=150,
output_path=output_path, burnin=0.6,
n_extra_spots=0)
#
# Subtract out a transit model
transit_model = generate_lc_depth(lc.times_jd, depth, hat11_params)
residuals = lc.fluxes - transit_model
# Find peaks in the light curve residuals
best_fit_spot_params = peak_finder(
lc.times.jd,
residuals,
lc.errors,
hat11_params,
n_peaks=4,
plots=False,
verbose=True)
best_fit_gaussian_model = summed_gaussians(lc.times.jd,
best_fit_spot_params)
# If spots are detected:
if best_fit_spot_params is not None:
output_path = os.path.join(output_dir,
'chains{0:03d}.hdf5'.format(transit_number))
sampler = run_emcee_seeded(
lc,
hat11_params,
best_fit_spot_params,
n_steps=5000,
n_walkers=200,
n_threads=32,
output_path=output_path,
burnin=0.5,
n_extra_spots=1)
