def fetch_masker(masker_path,
language,
path_to_fmridata,
path_to_input,
smoothing_fwhm=None,
logger=None):
""" Fetch or compute if needed a global masker from all subjects of a
given language.
Arguments:
- masker_path: str
- language: str
- path_to_input: str
- path_to_fmridata: str
- smoothing_fwhm: int
- logger: Logger
"""
if os.path.exists(masker_path + '.nii.gz') and os.path.exists(masker_path +
'.yml'):
logger.report_state(" loading existing masker...")
params = read_yaml(masker_path + '.yml')
mask_img = nib.load(masker_path + '.nii.gz')
masker = MultiNiftiMasker()
masker.set_params(**params)
masker.fit([mask_img])
else:
logger.report_state(" recomputing masker...")
fmri_runs = {}
subjects = [
get_subject_name(id) for id in possible_subjects_id(language)
]
for subject in subjects:
_, fmri_paths = fetch_data(path_to_fmridata, path_to_input,
subject, language)
fmri_runs[subject] = fmri_paths
masker = compute_global_masker(list(fmri_runs.values()),
smoothing_fwhm=smoothing_fwhm)
params = masker.get_params()
params = {
key: params[key]
for key in [
'detrend', 'dtype', 'high_pass', 'low_pass', 'mask_strategy',
'memory_level', 'n_jobs', 'smoothing_fwhm', 'standardize',
't_r', 'verbose'
]
}
nib.save(masker.mask_img_, masker_path + '.nii.gz')
save_yaml(params, masker_path + '.yml')
return masker
def create_raw_rest_data(imgs_list,
root,
raw_dir,
masker_params=None,
n_jobs=1,
mock=False,
memory=Memory(cachedir=None),
overwrite=False):
"""
Parameters
----------
memory
imgs_list: DataFrame with columns filename, confounds
root
raw_dir
masker_params
n_jobs
mock
Returns
-------
"""
if masker_params is None:
masker_params = {}
masker = MultiNiftiMasker(verbose=1,
memory=memory,
memory_level=1,
**masker_params)
if masker.mask_img is None:
masker.fit(imgs_list['filename'])
else:
masker.fit()
if 'confounds' in imgs_list.columns:
confounds = imgs_list['confounds']
imgs_list.rename(columns={'confounds': 'orig_confounds'})
else:
confounds = repeat(None)
if not os.path.exists(raw_dir):
os.makedirs(raw_dir)
filenames = Parallel(n_jobs=n_jobs)(delayed(_unmask_single_img)(
masker, imgs, confounds, root, raw_dir, mock=mock, overwrite=overwrite)
for imgs, confounds in zip(
imgs_list['filename'], confounds))
imgs_list = imgs_list.rename(columns={'filename': 'orig_filename'})
imgs_list = imgs_list.assign(filename=filenames)
imgs_list = imgs_list.assign(confounds=None)
if not mock:
imgs_list.to_csv(os.path.join(raw_dir, 'data.csv'), mode='w+')
mask_img_file = os.path.join(raw_dir, 'mask_img.nii.gz')
masker.mask_img_.to_filename(mask_img_file)
params = masker.get_params()
params.pop('memory')
params.pop('memory_level')
params.pop('n_jobs')
params.pop('verbose')
params['mask_img'] = mask_img_file
json.dump(params, open(os.path.join(raw_dir, 'masker.json'), 'w+'))
