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 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_
