# Read from command line argument
transit_number = int(sys.argv[1])
print('Transit number: {0}'.format(transit_number))
lc = transits[transit_number]
lc.delete_outliers()
lc.subtract_polynomial_baseline(order=2, params=transit_params)
lc.fluxes += quarterly_maxes[lc.quarters[0]]
lc.fluxes /= quarterly_maxes[lc.quarters[0]]
lc.errors /= quarterly_maxes[lc.quarters[0]]
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)
# Subtract out a transit model
transit_model = generate_lc_depth(lc.times_jd, depth, transit_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,
transit_params, n_peaks=6, plots=False,
verbose=True)
# This first spot model might miss the broadest spots
best_fit_gaussian_model = summed_gaussians(lc.times.jd,
best_fit_spot_params)
# # Run again on the residuals to pick up big spots
# best_fit_spot_params = peak_finder(lc.times.jd, residuals - best_fit_gaussian_model, lc.errors,
# transit_params, n_peaks=4, plots=True,
# verbose=True, broaden_gaussian_factor=2)
# Read from command line argument
transit_number = int(sys.argv[1])
lc = transits[transit_number]
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,
kepler17_params = kepler17_params_db()
# Construct light curve object from the raw data
whole_lc = LightCurve.from_raw_fits(light_curve_paths, name='Kepler17')
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] = []
