def prepare_data(dataset, prog_args, train=False, pre_process=None):
'''
preprocess TU dataset according to DiffPool's paper setting and load dataset into dataloader
'''
if train:
shuffle = True
else:
shuffle = False
if pre_process:
pre_process(dataset, prog_args)
# dataset.set_fold(fold)
return dgl.dataloading.GraphDataLoader(dataset,
batch_size=prog_args.batch_size,
shuffle=shuffle,
num_workers=prog_args.n_worker)
def prepare_data(dataset, prog_args, train=False, pre_process=None):
'''
preprocess TU dataset according to DiffPool's paper setting and load dataset into dataloader
'''
if train:
shuffle = True
else:
shuffle = False
if pre_process:
pre_process(dataset, prog_args)
# dataset.set_fold(fold)
return torch.utils.data.DataLoader(dataset,
batch_size=prog_args.batch_size,
shuffle=shuffle,
collate_fn=collate_fn,
drop_last=True,
num_workers=prog_args.n_worker)
rv_time_def = 'utc->utc'
################################################
# ---------- DATA PRE-PROCESSING ------------- #
# First, get the transit and RV data:
t_tr,f,f_err,transit_instruments,t_rv,rv,rv_err,rv_instruments = general_utils.read_data(target,mode,transit_time_def,rv_time_def)
# Initialize the parameters:
parameters = general_utils.read_priors(target,transit_instruments,rv_instruments,mode)
# Pre-process the transit data if available:
if mode != 'rvs':
t_tr,phases,f, f_err = data_utils.pre_process(t_tr,f,f_err,phot_detrend,\
phot_get_outliers,n_ommit,\
window,parameters,ld_law, mode)
if resampling:
# Define indexes between which data will be resampled:
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, \
################################################
# ---------- DATA PRE-PROCESSING ------------- #
# First, get the transit and RV data:
t_tr, f, f_err, transit_instruments, t_rv, rv, rv_err, rv_instruments = general_utils.read_data(
target, mode, transit_time_def, rv_time_def)
# Initialize the parameters:
parameters = general_utils.read_priors(target, transit_instruments,
rv_instruments, mode)
# Pre-process the transit data if available:
if mode != 'rvs':
t_tr,phases,f, f_err = data_utils.pre_process(t_tr,f,f_err,phot_detrend,\
phot_get_outliers,n_ommit,\
window,parameters,ld_law, mode)
if resampling:
# Define indexes between which data will be resampled:
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 PRE-PROCESSING ------------- #
# First, get the transit and RV data:
t_tr, f, f_err, transit_instruments, t_rv, rv, rv_err, rv_instruments = general_utils.read_data(
options)
# Sort transit data if there is any:
# Initialize the parameters:
parameters = general_utils.read_priors(options['TARGET'], options['MODE'])
# Pre-process the transit data if available:
if options['MODE'] != 'rvs':
t_tr, phases, f, f_err, transit_instruments = data_utils.pre_process(
t_tr, f, f_err, options, transit_instruments, parameters)
idx = np.argsort(t_tr)
t_tr = t_tr[idx]
f = f[idx]
f_err = f_err[idx]
phases = phases[idx]
transit_instruments = transit_instruments[idx]
idx_resampling = {}
for instrument in options['photometry'].keys():
idx = np.where(transit_instruments == instrument)[0]
if options['photometry'][instrument]['RESAMPLING']:
# Define indexes between which data will be resampled:
idx_resampling[instrument] = np.where((phases[idx]>-options['photometry'][instrument]['PHASE_MAX_RESAMPLING'])&\
(phases[idx]<options['photometry'][instrument]['PHASE_MAX_RESAMPLING']))[0]
else:
idx_resampling[instrument] = []