def _load_subject(ppath, url, credentials, cache_directory, df, sid):
'''
Creates a pseudo-path for the given subject and loads that subject as an hcp subject then
returns it.
'''
from neuropythy.hcp import subject
from posixpath import join as urljoin
pm = ppath._path_data['pathmod']
creds = ppath.credentials
# Make the pseudo-path; if auto-downloading is set to false, we want to create a pseudo-path
# from only the cache path
if config['hcp_auto_download'] in [False, None]:
ppath = pseudo_path(os.path.join(cache_directory, str(sid)), delete=False)
else:
ppath = pseudo_path((pm.s3fs, urljoin(url, str(sid))),
credentials=credentials,
cache_path=os.path.join(cache_directory, str(sid)),
delete=False) # the base dir will be deleted if needs be
return subject(ppath, default_alignment=df, name=str(sid))
def calc_arguments(args):
'''
calc_arguments is a calculator that parses the command-line arguments for the registration
command and produces the subject, the model, the log function, and the additional options.
'''
(args, opts) = _retinotopy_parser(args)
# We do some of the options right here...
if opts['help']:
print(info, file=sys.stdout)
sys.exit(1)
# and if we are verbose, lets setup a note function
verbose = opts['verbose']
def note(s):
if verbose:
print(s, file=sys.stdout)
sys.stdout.flush()
return verbose
def error(s):
print(s, file=sys.stderr)
sys.stderr.flush()
sys.exit(1)
if len(args) < 1: error('subject argument is required')
try: # we try FreeSurfer first:
import neuropythy.freesurfer as fs
# Add the subjects directory, if there is one
if 'subjects_dir' in opts and opts['subjects_dir'] is not None:
fs.add_subject_path(opts['subjects_dir'])
# Get the subject now
sub = fs.subject(args[0])
except Exception: sub = None
if sub is None:
try: # As an alternative, try HCP
import neuropythy.hcp as hcp
# Add the subjects directory, if there is one
if 'subjects_dir' in opts and opts['subjects_dir'] is not None:
hcp.add_subject_path(opts['subjects_dir'])
sub = hcp.subject(args[0])
except Exception: sub = None
if sub is None: error('Failed to load subject %s' % args[0])
# and the model
if len(args) > 1: mdl_name = args[1]
elif opts['model_sym']: mdl_name = 'schira'
else: mdl_name = 'benson17'
try:
if opts['model_sym']:
model = {h:retinotopy_model(mdl_name).persist() for h in ['lh', 'rh']}
else:
model = {h:retinotopy_model(mdl_name, hemi=h).persist() for h in ['lh', 'rh']}
except Exception: error('Could not load retinotopy model %s' % mdl_name)
# Now, we want to run a few filters on the options
# Parse the simple numbers
for o in ['weight_min', 'scale', 'max_step_size', 'max_out_eccen',
'max_in_eccen', 'min_in_eccen', 'field_sign_weight', 'radius_weight']:
opts[o] = float(opts[o])
opts['max_steps'] = int(opts['max_steps'])
# Make a note:
note('Processing subject: %s' % sub.name)
del opts['help']
del opts['verbose']
del opts['subjects_dir']
# That's all we need!
return pimms.merge(opts,
{'subject': sub.persist(),
'model': pyr.pmap(model),
'options': pyr.pmap(opts),
'note': note,
'error': error})