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
class IntraStats(object):
def __init__(self,
data_dir,
study_id,
model_id,
masker=None,
hrf_model='canonical with derivative',
drift_model='cosine',
glm_model='ar1',
contrast_type='t',
output_z=True,
output_stat=False,
output_effects=False,
output_variance=False,
merge_tasks=False,
resample=False,
target_affine=None,
target_shape=None,
memory=Memory(cachedir=None),
n_jobs=1):
self.data_dir = data_dir
self.study_id = study_id
self.model_id = model_id
if masker is None:
self.masker = MultiNiftiMasker()
else:
self.masker = masker
self.hrf_model = hrf_model
self.drift_model = drift_model
self.glm_model = glm_model
self.contrast_type = contrast_type
self.output_z = output_z
self.output_stat = output_stat
self.output_effects = output_effects
self.output_variance = output_variance
self.merge_tasks = merge_tasks
self.resample = resample
self.target_affine = target_affine
self.target_shape = target_shape
self.memory = memory
self.n_jobs = n_jobs
def fit(self, catalog, subjects_id):
doc = catalog[0]
self.task_contrasts_ = doc.get('contrasts')
self.condition_key_ = check_experimental_conditions(catalog)
self.run_key_ = doc.get('runs')
self.task_key_ = doc.get('tasks')
if subjects_id is not None:
self.subject_key_ = dict(
zip(subjects_id, [doc['subject_id'] for doc in catalog]))
else:
self.subject_key_ = [doc['subject_id'] for doc in catalog]
return self
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 fit_transform(self, catalog, subjects_id):
return self.fit(catalog, subjects_id).transform(catalog, subjects_id)
class IntraStats(object):
def __init__(self, data_dir, study_id, model_id,
masker=None,
hrf_model='canonical with derivative',
drift_model='cosine', glm_model='ar1',
contrast_type='t', output_z=True, output_stat=False,
output_effects=False, output_variance=False,
merge_tasks=False, resample=False, target_affine=None,
target_shape=None, memory=Memory(cachedir=None), n_jobs=1):
self.data_dir = data_dir
self.study_id = study_id
self.model_id = model_id
if masker is None:
self.masker = MultiNiftiMasker()
else:
self.masker = masker
self.hrf_model = hrf_model
self.drift_model = drift_model
self.glm_model = glm_model
self.contrast_type = contrast_type
self.output_z = output_z
self.output_stat = output_stat
self.output_effects = output_effects
self.output_variance = output_variance
self.merge_tasks = merge_tasks
self.resample = resample
self.target_affine = target_affine
self.target_shape = target_shape
self.memory = memory
self.n_jobs = n_jobs
def fit(self, catalog, subjects_id):
doc = catalog[0]
self.task_contrasts_ = doc.get('contrasts')
self.condition_key_ = check_experimental_conditions(catalog)
self.run_key_ = doc.get('runs')
self.task_key_ = doc.get('tasks')
if subjects_id is not None:
self.subject_key_ = dict(
zip(subjects_id, [doc['subject_id'] for doc in catalog]))
else:
self.subject_key_ = [doc['subject_id'] for doc in catalog]
return self
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 fit_transform(self, catalog, subjects_id):
return self.fit(catalog, subjects_id).transform(catalog, subjects_id)
contrast_values.append(session_con)
return contrast_values
if __name__ == '__main__':
from nignore.openfmri import Loader, glob_subjects_dirs
from nignore.spm import IntraEncoder
n_jobs = 1
root_dir = '/media/ys218403/mobile/brainpedia/preproc'
result_dir = '/home/ys218403/Data/intra_stats'
loader = Loader(model_id='model001')
encoder = IntraEncoder()
masker = MultiNiftiMasker(mask='mask.nii.gz', standardize=True,
smoothing_fwhm=6, n_jobs=n_jobs)
def sanitize_contrast(contrast, insert_derivative=True):
angry_contrasts = {}
for contrast_id in contrasts:
if ('house_vs_baseline' in contrast_id
or 'face_vs_baseline' in contrast_id
or 'face_vs_house' in contrast_id):
contrast = []
for session_con in contrasts[contrast_id]:
if session_con is not None:
session_con = np.array(session_con)
session_con = np.insert(
session_con,
