transits = LightCurve(**whole_lc.mask_out_of_transit(hat11_params)
).get_transit_light_curves(hat11_params)
delta_chi2 = {}
with ProgressBar(len(transits)) as bar:
for i, lc in enumerate(transits):
# Remove linear out-of-transit trend from transit
lc.remove_linear_baseline(hat11_params)
# 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_params = peak_finder(lc.times.jd, residuals, lc.errors,
hat11_params, n_peaks=2)
best_fit_gaussian_model = summed_gaussians(lc.times.jd, best_fit_params)
# Measure delta chi^2
chi2_transit = np.sum((lc.fluxes - transit_model)**2 /
lc.errors**2)/len(lc.fluxes)
if best_fit_params is not None:
split_input_parameters = np.split(np.array(best_fit_params),
len(best_fit_params)/3)
delta_chi2[i] = []
for amplitude, t0, sigma in split_input_parameters:
model_i = gaussian(lc.times.jd, amplitude, t0, sigma)
chi2_bumps = np.sum((lc.fluxes - transit_model - model_i)**2 /
lc.errors**2)/len(lc.fluxes)
delta_chi2[i].append(np.abs(chi2_transit - chi2_bumps))
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)
#
transits = LightCurve(**whole_lc.mask_out_of_transit(kepler17_params)
).get_transit_light_curves(kepler17_params)
delta_chi2 = {}
with ProgressBar(len(transits)) as bar:
for i, lc in enumerate(transits):
# Remove linear out-of-transit trend from transit
lc.remove_linear_baseline(kepler17_params)
# Subtract out a transit model
transit_model = generate_lc_depth(lc.times_jd, depth, kepler17_params)
residuals = lc.fluxes - transit_model
# Find peaks in the light curve residuals
best_fit_params = peak_finder(lc.times.jd, residuals, lc.errors,
kepler17_params)
best_fit_gaussian_model = summed_gaussians(lc.times.jd, best_fit_params)
# Measure delta chi^2
chi2_transit = np.sum((lc.fluxes - transit_model)**2 /
lc.errors**2)/len(lc.fluxes)
if best_fit_params is not None:
split_input_parameters = np.split(np.array(best_fit_params),
len(best_fit_params)/3)
delta_chi2[i] = []
for amplitude, t0, sigma in split_input_parameters:
model_i = gaussian(lc.times.jd, amplitude, t0, sigma)
chi2_bumps = np.sum((lc.fluxes - transit_model - model_i)**2 /
lc.errors**2)/len(lc.fluxes)
delta_chi2[i].append(np.abs(chi2_transit - chi2_bumps))