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)
