delta_chi2 = {}#np.zeros(len(transits))
with ProgressBar(len(transits)) as bar:
for i, lc in enumerate(transits):
#lc.plot()
# Remove linear out-of-transit trend from transit
lc.remove_linear_baseline(kepler17_params)
residuals = lc.fluxes - generate_lc_depth(lc.times_jd, depth, kepler17_params)
best_fit_params = peak_finder(lc.times.jd, residuals, lc.errors,
kepler17_params)
transit_model = generate_lc_depth(lc.times_jd, depth, kepler17_params)
chi2_transit = np.sum((lc.fluxes - transit_model)**2/lc.errors**2)/len(lc.fluxes)
gaussian_model = summed_gaussians(lc.times.jd, best_fit_params)
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))
if plots:
fig, ax = plt.subplots(3, 1, figsize=(8, 14), sharex=True)
ax[0].errorbar(lc.times.jd, lc.fluxes, lc.errors, fmt='.', color='k')
ax[0].plot(lc.times.jd, transit_model, 'r')
ax[0].set(ylabel='Flux')
lc = transits[104]
# Remove linear out-of-transit trend from transit
lc.remove_linear_baseline()
depth = 0.13413993**2
residuals = lc.fluxes - generate_lc_depth(lc.times_jd, depth)
# Fit the transit model residuals for n_peaks gaussians
sampler, samples = run_emcee(lc.times.jd, residuals, lc.errors, n_peaks=4)
# Plot the results
import corner
corner.corner(samples)
median_params = np.median(samples, axis=0)
transit_model = generate_lc_depth(lc.times_jd, depth)
gaussian_model = summed_gaussians(lc.times.jd, median_params)
fig, ax = plt.subplots(3, 1, figsize=(8, 14), sharex=True)
ax[0].errorbar(lc.times.jd, lc.fluxes, lc.errors, fmt='.', color='k')
ax[0].plot(lc.times.jd, transit_model, 'r')
ax[0].set(ylabel='Flux')
ax[1].errorbar(lc.times.jd, lc.fluxes - transit_model, fmt='.', color='k')
ax[1].errorbar(lc.times.jd, gaussian_model, fmt='.', color='r')
ax[1].axhline(0, color='r')
ax[1].set_ylabel('Transit Residuals')
ax[2].errorbar(lc.times.jd, lc.fluxes - transit_model - gaussian_model, fmt='.', color='k')
ax[2].axhline(0, color='r')
ax[2].set_ylabel('Gaussian Residuals')