def save_maps(model_dir,
doc,
resample=False,
target_affine=None,
target_shape=None):
for dtype in ['c_maps', 't_maps']:
if dtype in doc:
maps_dir = make_dir(model_dir, dtype, strict=False)
for key in doc[dtype]:
fname = '%s.nii.gz' % safe_name(key.lower())
img = nb.load(doc[dtype][key])
if resample:
img = resample_img(img, target_affine, target_shape)
nb.save(img, os.path.join(maps_dir, fname))
if 'beta_maps' in doc:
maps_dir = make_dir(model_dir, 'beta_maps')
for path in doc['beta_maps']:
fname = '%s.nii.gz' % safe_name(
os.path.split(path)[1].lower().split('.')[0])
img = nb.load(path)
if resample:
img = resample_img(img,
target_affine,
target_shape,
copy=False)
nb.save(img, os.path.join(maps_dir, fname))
if 'mask' in doc:
img = nb.load(doc['mask'])
if resample:
img = resample_img(img,
target_affine,
target_shape,
interpolation='nearest',
copy=False)
nb.save(img, os.path.join(model_dir, 'mask.nii.gz'))
def save_maps(model_dir, doc, resample=False,
target_affine=None, target_shape=None):
for dtype in ['c_maps', 't_maps']:
if dtype in doc:
maps_dir = make_dir(model_dir, dtype, strict=False)
for key in doc[dtype]:
fname = '%s.nii.gz' % safe_name(key.lower())
img = nb.load(doc[dtype][key])
if resample:
img = resample_img(img, target_affine, target_shape)
nb.save(img, os.path.join(maps_dir, fname))
if 'beta_maps' in doc:
maps_dir = make_dir(model_dir, 'beta_maps')
for path in doc['beta_maps']:
fname = '%s.nii.gz' % safe_name(os.path.split(
path)[1].lower().split('.')[0])
img = nb.load(path)
if resample:
img = resample_img(
img, target_affine, target_shape, copy=False)
nb.save(img, os.path.join(maps_dir, fname))
if 'mask' in doc:
img = nb.load(doc['mask'])
if resample:
img = resample_img(img, target_affine, target_shape,
interpolation='nearest', copy=False)
nb.save(img, os.path.join(model_dir, 'mask.nii.gz'))
def transform(self, catalog, subjects_id):
catalog_ = copy.deepcopy(catalog)
study_dir = make_dir(self.data_dir, self.study_id, strict=False)
if isinstance(self.subject_key_, dict):
save_table(self.subject_key_,
os.path.join(study_dir, 'subject_key.txt'))
save_table(self.task_key_, os.path.join(study_dir, 'task_key.txt'),
merge=self.merge_tasks)
save_table({'TR': catalog_[0]['tr']},
os.path.join(study_dir, 'scan_key.txt'))
model_dir = make_dir(study_dir, 'models', self.model_id, strict=False)
save_task_contrasts(model_dir, catalog_[0], merge=self.merge_tasks)
save_condition_key(model_dir, catalog_[0], merge=self.merge_tasks)
n_jobs = -1 if self.n_jobs != 1 else 1
self.encoder_ = IntraEncoder(hrf_model=self.hrf_model,
drift_model=self.drift_model,
memory=self.memory,
n_jobs=n_jobs)
all_niimgs = self.encoder_.fit_transform(catalog_, subjects_id)
if subjects_id is None:
subjects_id = [doc['subject_id'] for doc in catalog]
outputs = Parallel(n_jobs=self.n_jobs)(
delayed(_compute_glm)(
LinearModeler(masker=self.masker,
reporter=os.path.join(
study_dir, subject_id,
'model', self.model_id),
glm_model=self.glm_model,
hrf_model=self.hrf_model,
contrast_type=self.contrast_type,
output_z=self.output_z,
output_stat=self.output_stat,
output_effects=self.output_effects,
output_variance=self.output_variance),
niimgs=niimgs,
design_matrices=design_matrices,
contrasts=doc['contrasts'])
for subject_id, doc, niimgs, design_matrices in zip(
subjects_id,
catalog_,
all_niimgs,
self.encoder_.design_matrices_))
if self.resample:
Parallel(n_jobs=n_jobs)(
delayed(_resample_img)(
doc[dtype][cid], self.target_affine, self.target_shape, )
for doc in outputs for dtype in doc for cid in doc[dtype])
return outputs
def transform(self, catalog, subjects_id):
catalog_ = copy.deepcopy(catalog)
study_dir = make_dir(self.data_dir, self.study_id, strict=False)
if isinstance(self.subject_key_, dict):
save_table(self.subject_key_,
os.path.join(study_dir, 'subject_key.txt'))
save_table(self.task_key_,
os.path.join(study_dir, 'task_key.txt'),
merge=self.merge_tasks)
save_table({'TR': catalog_[0]['tr']},
os.path.join(study_dir, 'scan_key.txt'))
model_dir = make_dir(study_dir, 'models', self.model_id, strict=False)
save_task_contrasts(model_dir, catalog_[0], merge=self.merge_tasks)
save_condition_key(model_dir, catalog_[0], merge=self.merge_tasks)
n_jobs = -1 if self.n_jobs != 1 else 1
self.encoder_ = IntraEncoder(hrf_model=self.hrf_model,
drift_model=self.drift_model,
memory=self.memory,
n_jobs=n_jobs)
all_niimgs = self.encoder_.fit_transform(catalog_, subjects_id)
if subjects_id is None:
subjects_id = [doc['subject_id'] for doc in catalog]
outputs = Parallel(n_jobs=self.n_jobs)(
delayed(_compute_glm)(LinearModeler(
masker=self.masker,
reporter=os.path.join(study_dir, subject_id, 'model',
self.model_id),
glm_model=self.glm_model,
hrf_model=self.hrf_model,
contrast_type=self.contrast_type,
output_z=self.output_z,
output_stat=self.output_stat,
output_effects=self.output_effects,
output_variance=self.output_variance),
niimgs=niimgs,
design_matrices=design_matrices,
contrasts=doc['contrasts'])
for subject_id, doc, niimgs, design_matrices in zip(
subjects_id, catalog_, all_niimgs,
self.encoder_.design_matrices_))
if self.resample:
Parallel(n_jobs=n_jobs)(delayed(_resample_img)(
doc[dtype][cid],
self.target_affine,
self.target_shape,
) for doc in outputs for dtype in doc for cid in doc[dtype])
return outputs
def save_raw(subject_dir, doc):
if 'bold' in doc:
run_key = doc['runs']
for label, session_data in zip(run_key, doc['bold']):
if isinstance(session_data, (list, np.ndarray)):
img = nb.concat_images(session_data, check_affines=False)
else:
img = nb.load(session_data)
session_dir = make_dir(subject_dir, 'BOLD', label, strict=False)
nb.save(img, os.path.join(session_dir, 'bold.nii.gz'))
if 'anatomy' in doc:
anat_dir = make_dir(subject_dir, 'anatomy', strict=False)
img = nb.load(doc['anatomy'])
nb.save(img, os.path.join(anat_dir, 'highres001.nii.gz'))
def save_raw(subject_dir, doc):
if 'bold' in doc:
run_key = doc['runs']
for label, session_data in zip(run_key, doc['bold']):
if isinstance(session_data, (list, np.ndarray)):
img = nb.concat_images(session_data, check_affines=False)
else:
img = nb.load(session_data)
session_dir = make_dir(subject_dir, 'BOLD', label, strict=False)
nb.save(img, os.path.join(session_dir, 'bold.nii.gz'))
if 'anatomy' in doc:
anat_dir = make_dir(subject_dir, 'anatomy', strict=False)
img = nb.load(doc['anatomy'])
nb.save(img, os.path.join(anat_dir, 'highres001.nii.gz'))
def save_preproc(model_dir, doc):
if 'swabold' in doc:
run_key = doc['runs']
for label, session_data, motion in zip(run_key, doc['swabold'],
doc['motion']):
if isinstance(session_data, (list, np.ndarray)):
img = nb.concat_images(session_data)
else:
img = nb.load(session_data)
session_dir = make_dir(model_dir, 'BOLD', label)
nb.save(img, os.path.join(session_dir, 'bold.nii.gz'))
if isinstance(motion, (str, unicode)):
shutil.copyfile(motion, os.path.join(session_dir,
'motion.txt'))
else:
np.savetxt(os.path.join(session_dir, 'motion.txt'), motion)
if 'wmanatomy' in doc:
anat_dir = make_dir(model_dir, 'anatomy')
img = nb.load(doc['wmanatomy'])
nb.save(img, os.path.join(anat_dir, 'highres001_brain.nii.gz'))
def save_preproc(model_dir, doc):
if 'swabold' in doc:
run_key = doc['runs']
for label, session_data, motion in zip(
run_key, doc['swabold'], doc['motion']):
if isinstance(session_data, (list, np.ndarray)):
img = nb.concat_images(session_data)
else:
img = nb.load(session_data)
session_dir = make_dir(model_dir, 'BOLD', label)
nb.save(img, os.path.join(session_dir, 'bold.nii.gz'))
if isinstance(motion, (str, unicode)):
shutil.copyfile(
motion, os.path.join(session_dir, 'motion.txt'))
else:
np.savetxt(os.path.join(session_dir, 'motion.txt'), motion)
if 'wmanatomy' in doc:
anat_dir = make_dir(model_dir, 'anatomy')
img = nb.load(doc['wmanatomy'])
nb.save(img, os.path.join(anat_dir, 'highres001_brain.nii.gz'))
def transform(self, catalog, subjects_id):
catalog_ = copy.deepcopy(catalog)
study_dir = make_dir(self.data_dir, self.study_id, strict=False)
if isinstance(self.subject_key_, dict):
save_table(self.subject_key_,
os.path.join(study_dir, 'subject_key.txt'))
save_table(self.task_key_,
os.path.join(study_dir, 'task_key.txt'),
merge=self.merge_tasks)
save_table({'TR': catalog[0]['tr']},
os.path.join(study_dir, 'scan_key.txt'))
model_dir = make_dir(study_dir, 'models', self.model_id, strict=False)
save_task_contrasts(model_dir, catalog_[0], merge=self.merge_tasks)
save_condition_key(model_dir, catalog_[0], merge=self.merge_tasks)
if subjects_id is None:
subjects_id = [doc['subject_id'] for doc in catalog]
Parallel(n_jobs=self.n_jobs)(
delayed(save_maps)(os.path.join(study_dir, subject_id, 'model',
self.model_id), doc, self.resample,
self.target_affine, self.target_shape)
for subject_id, doc in zip(subjects_id, catalog_))
Parallel(n_jobs=self.n_jobs)(
delayed(save_preproc)(os.path.join(study_dir, subject_id, 'model',
self.model_id), doc)
for subject_id, doc in zip(subjects_id, catalog_))
Parallel(n_jobs=self.n_jobs)(
delayed(save_raw)(os.path.join(study_dir, subject_id), doc)
for subject_id, doc in zip(subjects_id, catalog_))
Parallel(n_jobs=self.n_jobs)(
delayed(save_onsets)(os.path.join(study_dir, subject_id, 'model',
self.model_id, 'onsets'), doc)
for subject_id, doc in zip(subjects_id, catalog_))
return catalog_
def transform(self, catalog, subjects_id):
catalog_ = copy.deepcopy(catalog)
study_dir = make_dir(self.data_dir, self.study_id, strict=False)
if isinstance(self.subject_key_, dict):
save_table(self.subject_key_,
os.path.join(study_dir, 'subject_key.txt'))
save_table(self.task_key_, os.path.join(study_dir, 'task_key.txt'),
merge=self.merge_tasks)
save_table({'TR': catalog[0]['tr']},
os.path.join(study_dir, 'scan_key.txt'))
model_dir = make_dir(study_dir, 'models', self.model_id, strict=False)
save_task_contrasts(model_dir, catalog_[0], merge=self.merge_tasks)
save_condition_key(model_dir, catalog_[0], merge=self.merge_tasks)
if subjects_id is None:
subjects_id = [doc['subject_id'] for doc in catalog]
Parallel(n_jobs=self.n_jobs)(delayed(save_maps)(
os.path.join(study_dir, subject_id, 'model', self.model_id),
doc, self.resample, self.target_affine, self.target_shape)
for subject_id, doc in zip(subjects_id, catalog_))
Parallel(n_jobs=self.n_jobs)(delayed(save_preproc)(
os.path.join(study_dir, subject_id,
'model', self.model_id), doc)
for subject_id, doc in zip(subjects_id, catalog_))
Parallel(n_jobs=self.n_jobs)(delayed(save_raw)(
os.path.join(study_dir, subject_id), doc)
for subject_id, doc in zip(subjects_id, catalog_))
Parallel(n_jobs=self.n_jobs)(delayed(save_onsets)(
os.path.join(study_dir, subject_id,
'model', self.model_id, 'onsets'), doc)
for subject_id, doc in zip(subjects_id, catalog_))
return catalog_
def save_onsets(onsets_dir, doc, merge=False):
run_key = doc.get('runs')
if 'onsets' in doc:
for session_id, session in zip(run_key, doc['onsets']):
if not merge:
del_dir(onsets_dir, session_id)
session_dir = make_dir(onsets_dir, session_id, strict=False)
for onset in session:
cond_id = onset[0]
values = [str(v) for v in onset[1:]]
with open(os.path.join(session_dir, '%s.txt' % cond_id),
'a') as f:
writer = csv.writer(f, delimiter=' ', quotechar='"')
writer.writerow(values)
def save_onsets(onsets_dir, doc, merge=False):
run_key = doc.get('runs')
if 'onsets' in doc:
for session_id, session in zip(run_key, doc['onsets']):
if not merge:
del_dir(onsets_dir, session_id)
session_dir = make_dir(onsets_dir, session_id, strict=False)
for onset in session:
cond_id = onset[0]
values = [str(v) for v in onset[1:]]
with open(os.path.join(session_dir,
'%s.txt' % cond_id), 'a') as f:
writer = csv.writer(f, delimiter=' ', quotechar='"')
writer.writerow(values)
