num_subjs = int(sys.argv[6])
# the following line is an example to leaving a subject out
#vs = VoxelSelector(labels[0:204], epochs_per_subj, num_subjs-1, raw_data[0:204])
# if using all subjects
vs = VoxelSelector(labels, epochs_per_subj, num_subjs, raw_data)
# if providing two masks, just append raw_data2 as the last input argument
#vs = VoxelSelector(labels, epochs_per_subj, num_subjs, raw_data, raw_data2=raw_data2)
# for cross validation, use SVM with precomputed kernel
clf = svm.SVC(kernel='precomputed', shrinking=False, C=10)
results = vs.run(clf)
# this output is just for result checking
if MPI.COMM_WORLD.Get_rank() == 0:
logger.info('correlation-based voxel selection is done')
#print(results[0:100])
mask_img = nib.load(mask_file)
mask = mask_img.get_data().astype(np.bool)
score_volume = np.zeros(mask.shape, dtype=np.float32)
score = np.zeros(len(results), dtype=np.float32)
seq_volume = np.zeros(mask.shape, dtype=np.int)
seq = np.zeros(len(results), dtype=np.int)
with open('result_list.txt', 'w') as fp:
for idx, tuple in enumerate(results):
fp.write(str(tuple[0]) + ' ' + str(tuple[1]) + '\n')
score[tuple[0]] = tuple[1]
seq[tuple[0]] = idx
score_volume[mask] = score
seq_volume[mask] = seq
io.save_as_nifti_file(score_volume, mask_img.affine,
'result_score.nii.gz')
io.save_as_nifti_file(seq_volume, mask_img.affine, 'result_seq.nii.gz')
def test_save_as_nifti_file(tmpdir) -> None:
out_file = str(tmpdir / "nifti.nii")
shape = (4, 4, 4)
io.save_as_nifti_file(np.ones(shape), np.eye(4), out_file)
assert nib.load(out_file).get_data().shape == shape
# RandomType.UNREPRODUCIBLE permutes the voxels differently across runs
# example
#from brainiak.fcma.preprocessing import RandomType
#data, labels = prepare_searchlight_mvpa_data(images, conditions,
# random=RandomType.UNREPRODUCIBLE)
# the following line is an example to leaving a subject out
#epoch_info = [x for x in epoch_info if x[1] != 0]
num_subjs = int(sys.argv[5])
# create a Searchlight object
sl = Searchlight(sl_rad=1)
mvs = MVPAVoxelSelector(data, mask, labels, num_subjs, sl)
clf = svm.SVC(kernel='linear', shrinking=False, C=1)
# only rank 0 has meaningful return values
score_volume, results = mvs.run(clf)
# this output is just for result checking
if MPI.COMM_WORLD.Get_rank()==0:
score_volume = np.nan_to_num(score_volume.astype(np.float))
io.save_as_nifti_file(score_volume, mask_image.affine,
'result_score.nii.gz')
seq_volume = np.zeros(mask.shape, dtype=np.int)
seq = np.zeros(len(results), dtype=np.int)
with open('result_list.txt', 'w') as fp:
for idx, tuple in enumerate(results):
fp.write(str(tuple[0]) + ' ' + str(tuple[1]) + '\n')
seq[tuple[0]] = idx
seq_volume[mask] = seq
io.save_as_nifti_file(seq_volume, mask_image.affine,
'result_seq.nii.gz')
def test_save_as_nifti_file(tmpdir) -> None:
out_file = str(tmpdir / "nifti.nii")
shape = (4, 4, 4)
io.save_as_nifti_file(np.ones(shape), np.eye(4), out_file)
assert nib.load(out_file).get_data().shape == shape