def test_read_activity_data():
masked_data1 = io.read_activity_data(dir, extension, mask_file)
raw_data = io.read_activity_data(dir, extension)
mask_img = nib.load(mask_file)
mask = mask_img.get_data().astype(np.bool)
masked_data2 = []
for data in raw_data:
masked_data2.append(data[mask])
assert len(masked_data1) == len(masked_data2), \
'numbers of subjects do not match in test_read_activity_data'
for idx in range(len(masked_data1)):
assert np.allclose(masked_data1[idx], masked_data2[idx]), \
'masked data do not match in test_read_activity_data'
def test_read_activity_data():
masked_data1 = io.read_activity_data(dir, extension, mask_file)
raw_data = io.read_activity_data(dir, extension)
mask_img = nib.load(mask_file)
mask = mask_img.get_data().astype(np.bool)
masked_data2 = []
for data in raw_data:
masked_data2.append(data[mask])
assert len(masked_data1) == len(masked_data2), \
'numbers of subjects do not match in test_read_activity_data'
for idx in range(len(masked_data1)):
assert np.allclose(masked_data1[idx], masked_data2[idx]), \
'masked data do not match in test_read_activity_data'
import scipy.io
format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
# if want to output log to a file instead of outputting log to the console,
# replace "stream=sys.stdout" with "filename='fcma.log'"
logging.basicConfig(level=logging.INFO, format=format, stream=sys.stdout)
logger = logging.getLogger(__name__)
# python corr_comp.py face_scene bet.nii.gz face_scene/prefrontal_top_mask.nii.gz face_scene/fs_epoch_labels.npy
if __name__ == '__main__':
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
raw_data = io.read_activity_data(data_dir, extension, mask_file)
epoch_list = np.load(epoch_file)
epoch_info = io.generate_epochs_info(epoch_list)
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 = {}
import scipy.io
format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
# if want to output log to a file instead of outputting log to the console,
# replace "stream=sys.stdout" with "filename='fcma.log'"
logging.basicConfig(level=logging.INFO, format=format, stream=sys.stdout)
logger = logging.getLogger(__name__)
# python corr_comp.py face_scene bet.nii.gz face_scene/prefrontal_top_mask.nii.gz face_scene/fs_epoch_labels.npy
if __name__ == '__main__':
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
raw_data = io.read_activity_data(data_dir, extension, mask_file)
epoch_list = np.load(epoch_file)
epoch_info = io.generate_epochs_info(epoch_list)
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)
if __name__ == '__main__':
if MPI.COMM_WORLD.Get_rank() == 0:
logger.info('programming starts in %d process(es)' %
MPI.COMM_WORLD.Get_size())
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
raw_data = []
# all MPI processes read the mask; the mask file is small
mask_img = nib.load(mask_file)
mask = mask_img.get_data().astype(np.bool)
epoch_info = None
if MPI.COMM_WORLD.Get_rank() == 0:
raw_data = io.read_activity_data(data_dir, extension)
epoch_list = np.load(epoch_file)
epoch_info = io.generate_epochs_info(epoch_list)
# 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(raw_data, mask, epoch_info, 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.write_nifti_file(score_volume, mask_img.affine,
"""
if __name__ == "__main__":
if MPI.COMM_WORLD.Get_rank() == 0:
logger.info("programming starts in %d process(es)" % MPI.COMM_WORLD.Get_size())
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
raw_data = []
# all MPI processes read the mask; the mask file is small
mask_img = nib.load(mask_file)
mask = mask_img.get_data().astype(np.bool)
epoch_info = None
if MPI.COMM_WORLD.Get_rank() == 0:
raw_data = io.read_activity_data(data_dir, extension)
epoch_list = np.load(epoch_file)
epoch_info = io.generate_epochs_info(epoch_list)
# 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(raw_data, mask, epoch_info, 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.write_nifti_file(score_volume, mask_img.affine, "result_score.nii.gz")
