def load_masker(path, **kwargs):
params = read_yaml(path + '.yml')
mask_img = nib.load(path + '.nii.gz')
masker = NiftiMasker(mask_img)
masker.set_params(**params)
if kwargs:
masker.set_params(**kwargs)
masker.fit()
return masker
def get_roi_mask(
atlas_maps,
index_mask,
labels,
path=None,
global_mask=None,
resample_to_img_=None,
intersect_with_img=False,
PROJECT_PATH="/neurospin/unicog/protocols/IRMf/LePetitPrince_Pallier_2018/LePetitPrince/"
):
"""Return the Niftimasker object for a given ROI based on an atlas.
Optionally resampled based on a resample_to_img_ image and another masker (global masker) parameters.
"""
if path is None:
check_folder(os.path.join(PROJECT_PATH, 'derivatives/fMRI/ROI_masks'))
path = os.path.join(
PROJECT_PATH, 'derivatives/fMRI/ROI_masks',
labels[index_mask]) #be careful to remove ‘background‘ from labels
if os.path.exists(path + '.nii.gz') and os.path.exists(path + '.yml'):
masker = load_masker(path,
resample_to_img_=resample_to_img_,
intersect_with_img=intersect_with_img)
else:
mask = math_img('img=={}'.format(index_mask + 1), img=atlas_maps)
if resample_to_img_ is not None:
mask = resample_to_img(mask,
resample_to_img_,
interpolation='nearest')
if intersect_with_img:
mask_img = math_img('img==2',
img=math_img('img1+img2',
img1=mask_img,
img2=resample_to_img_))
masker = NiftiMasker(mask)
if global_mask:
params = read_yaml(global_mask + '.yml')
params['detrend'] = False
params['standardize'] = False
masker.set_params(**params)
masker.fit()
save_masker(masker, path)
return masker
def load_masker(path,
resample_to_img_=None,
intersect_with_img=False,
**kwargs):
params = read_yaml(path + '.yml')
mask_img = nib.load(path + '.nii.gz')
if resample_to_img_ is not None:
mask_img = resample_to_img(mask_img,
resample_to_img_,
interpolation='nearest')
if intersect_with_img:
mask_img = math_img('img==2',
img=math_img('img1+img2',
img1=mask_img,
img2=resample_to_img_))
masker = NiftiMasker(mask_img)
masker.set_params(**params)
if kwargs:
masker.set_params(**kwargs)
masker.fit()
return masker
def get_roi_mask(atlas_maps, index_mask, labels, path=None, global_mask=None):
"""Return the Niftimasker object for a given ROI based on an atlas.
Optionally resampled based on a global masker.
"""
if path is None:
path = os.path.join(PROJECT_PATH, 'derivatives/fMRI/ROI_masks', labels[index_mask+1])
check_folder(path)
if os.path.exists(path + '.nii.gz') and os.path.exists(path + '.yml'):
masker = load_masker(path)
else:
mask = math_img('img > 50', img=index_img(atlas_maps, index_mask))
if global_mask:
global_masker = nib.load(global_mask + '.nii.gz')
mask = resample_to_img(mask, global_masker, interpolation='nearest')
params = read_yaml(global_mask + '.yml')
params['detrend'] = False
params['standardize'] = False
masker = NiftiMasker(mask)
if global_mask:
masker.set_params(**params)
masker.fit()
save_masker(masker, path)
return masker
FMRIDATA_PATH,
INPUT_PATH,
smoothing_fwhm=None,
logger=logger)
original_masker = global_masker_50
new_masker = global_masker_95
original_masker.set_params(detrend=False, standardize=False)
new_masker.set_params(detrend=False, standardize=False)
params = global_masker_95.get_params()
new_img = new_img_like(
global_masker_95.mask_img,
scipy.ndimage.binary_dilation(global_masker_95.mask_img.get_data()))
dilated_global_masker_95 = NiftiMasker()
dilated_global_masker_95.set_params(**params)
dilated_global_masker_95.mask_img = new_img
dilated_global_masker_95.fit()
atlas_maps, labels = reporting.load_atlas()
subject_names_list = [
utils.get_subject_name(sub_id)
for sub_id in utils.possible_subjects_id(language)
]
subject_ids_list = utils.possible_subjects_id(language)
def dilate_img(img):
if type(img) == str:
img = nib.load(img)
data = img.get_data()
