idx_resampling = np.where((phases>-phase_max)&(phases<phase_max))[0] else: idx_resampling = [] # Create results folder if not already created: if not os.path.exists('results'): os.mkdir('results') # If chains not ran, run the MCMC and save results: if not os.path.exists('results/'+target+'_'+mode+'_'+phot_noise_model+'_'+ld_law): data_utils.exonailer_mcmc_fit(t_tr, f, f_err, transit_instruments, t_rv, rv, rv_err, rv_instruments,\ parameters, ld_law, mode, rv_jitter = rv_jitter, \ njumps = njumps, nburnin = nburnin, \ nwalkers = nwalkers, noise_model = phot_noise_model,\ resampling = resampling, idx_resampling = idx_resampling,\ N_resampling = N_resampling) general_utils.save_results(target,mode,phot_noise_model,ld_law,parameters) else: parameters = general_utils.read_results(target,mode,phot_noise_model,ld_law,transit_instruments, rv_instruments) # Get plot of the transit-fit: if mode == 'transit': data_utils.plot_transit(t_tr,f,parameters,ld_law,transit_instruments) elif mode == 'full': data_utils.plot_transit_and_rv(t_tr,f,t_rv,rv,rv_err,parameters,ld_law,rv_jitter, \ transit_instruments, rv_instruments,\ resampling = resampling, phase_max = phase_max, \ N_resampling=N_resampling)
if not os.path.exists('results'): os.mkdir('results') # If chains not ran, run the MCMC and save results: if not os.path.exists('results/' + target + '_' + mode + '_' + phot_noise_model + '_' + ld_law): data_utils.exonailer_mcmc_fit(t_tr, f, f_err, transit_instruments, t_rv, rv, rv_err, rv_instruments,\ parameters, ld_law, mode, rv_jitter = rv_jitter, \ njumps = njumps, nburnin = nburnin, \ nwalkers = nwalkers, noise_model = phot_noise_model,\ resampling = resampling, idx_resampling = idx_resampling,\ N_resampling = N_resampling) general_utils.save_results(target, mode, phot_noise_model, ld_law, parameters) else: parameters = general_utils.read_results(target, mode, phot_noise_model, ld_law, transit_instruments, rv_instruments) # Get plot of the transit-fit: if mode == 'transit': data_utils.plot_transit(t_tr,f,parameters,ld_law,transit_instruments, resampling = resampling, \ phase_max = phase_max, N_resampling=N_resampling) elif mode == 'full': data_utils.plot_transit_and_rv(t_tr,f,t_rv,rv,rv_err,parameters,ld_law,rv_jitter, \ transit_instruments, rv_instruments,\ resampling = resampling, phase_max = phase_max, \ N_resampling=N_resampling)
else: idx_resampling = [] # Create results folder if not already created: if not os.path.exists('results'): os.mkdir('results') # If chains not ran, run the MCMC and save results: if not os.path.exists('results/'+target+'_'+mode+'_'+phot_noise_model+'_'+ld_law): data_utils.exonailer_mcmc_fit(t_tr, f, f_err, transit_instruments, t_rv, rv, rv_err, rv_instruments,\ parameters, ld_law, mode, rv_jitter = rv_jitter, \ njumps = njumps, nburnin = nburnin, \ nwalkers = nwalkers, noise_model = phot_noise_model,\ resampling = resampling, idx_resampling = idx_resampling,\ N_resampling = N_resampling) general_utils.save_results(target,mode,phot_noise_model,ld_law,parameters) else: parameters = general_utils.read_results(target,mode,phot_noise_model,ld_law,transit_instruments, rv_instruments) # Get plot of the transit-fit: if mode == 'transit': data_utils.plot_transit(t_tr,f,parameters,ld_law,transit_instruments, resampling = resampling, \ phase_max = phase_max, N_resampling=N_resampling) elif mode == 'full': data_utils.plot_transit_and_rv(t_tr,f,t_rv,rv,rv_err,parameters,ld_law,rv_jitter, \ transit_instruments, rv_instruments,\ resampling = resampling, phase_max = phase_max, \ N_resampling=N_resampling)