def test_multimask_images(images: Iterable[SpatialImage],
masks: Sequence[np.ndarray],
multimasked_data: Iterable[Iterable[np.ndarray]]
) -> None:
result = multimask_images(images, masks)
for mask_data in zip(result, multimasked_data):
for result_data, precomputed_data in zip(mask_data[0], mask_data[1]):
assert np.array_equal(result_data, precomputed_data)
def test_multimask_images(
images: Iterable[SpatialImage], masks: Sequence[np.ndarray],
multimasked_images: Iterable[Iterable[np.ndarray]]) -> None:
result = multimask_images(images, masks)
for result_images, expected_images in zip(result, multimasked_images):
for result_image, expected_image in zip(result_images,
expected_images):
assert np.array_equal(result_image, expected_image)
def test_multimask_images(
images: Iterable[SpatialImage],
masks: Sequence[np.ndarray],
multimasked_images: Iterable[Iterable[np.ndarray]]
) -> None:
result = multimask_images(images, masks)
for result_images, expected_images in zip(result,
multimasked_images):
for result_image, expected_image in zip(result_images,
expected_images):
assert np.array_equal(result_image, expected_image)
# python3 corr_comp.py face_scene bet.nii.gz face_scene/prefrontal_top_mask.nii.gz face_scene/fs_epoch_labels.npy
if __name__ == '__main__':
if len(sys.argv) != 5:
logger.error('the number of input argument is not correct')
sys.exit(1)
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
images = dataset.load_images_from_dir(data_dir, extension)
mask = dataset.load_boolean_mask(mask_file)
conditions = dataset.load_labels(epoch_file)
(raw_data, ) = image.multimask_images(images, (mask, ))
epoch_info = generate_epochs_info(conditions)
for idx, epoch in enumerate(epoch_info):
label = epoch[0]
sid = epoch[1]
start = epoch[2]
end = epoch[3]
mat = raw_data[sid][:, start:end]
mat = np.ascontiguousarray(mat, dtype=np.float32)
logger.info(
'start to compute correlation for subject %d epoch %d with label %d'
% (sid, idx, label))
corr = compute_correlation(mat, mat)
mdict = {}
mdict['corr'] = corr
# python3 corr_comp.py face_scene bet.nii.gz face_scene/prefrontal_top_mask.nii.gz face_scene/fs_epoch_labels.npy
if __name__ == '__main__':
if len(sys.argv) != 5:
logger.error('the number of input argument is not correct')
sys.exit(1)
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
images = dataset.load_images_from_dir(data_dir, extension)
mask = dataset.load_boolean_mask(mask_file)
conditions = dataset.load_labels(epoch_file)
(raw_data,) = image.multimask_images(images, (mask,))
epoch_info = generate_epochs_info(conditions)
for idx, epoch in enumerate(epoch_info):
label = epoch[0]
sid = epoch[1]
start = epoch[2]
end = epoch[3]
mat = raw_data[sid][:, start:end]
mat = np.ascontiguousarray(mat, dtype=np.float32)
logger.info(
'start to compute correlation for subject %d epoch %d with label %d' %
(sid, idx, label)
)
corr = compute_correlation(mat, mat)
mdict = {}
# python3 corr_comp.py face_scene bet.nii.gz face_scene/prefrontal_top_mask.nii.gz face_scene/fs_epoch_labels.npy
if __name__ == '__main__':
if len(sys.argv) != 5:
logger.error('the number of input argument is not correct')
sys.exit(1)
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
images = io.load_images_from_dir(data_dir, extension)
mask = io.load_boolean_mask(mask_file)
conditions = io.load_labels(epoch_file)
raw_data = list(image.multimask_images(images, (mask, )))
epoch_info = generate_epochs_info(conditions)
for idx, epoch in enumerate(epoch_info):
label = epoch[0]
sid = epoch[1]
start = epoch[2]
end = epoch[3]
mat = raw_data[sid][0][:, start:end]
mat = np.ascontiguousarray(mat, dtype=np.float32)
logger.info(
'start to compute correlation for subject %d epoch %d with label %d'
% (sid, idx, label))
corr = compute_correlation(mat, mat)
mdict = {}
mdict['corr'] = corr