def test_prepare_searchlight_mvpa_data():
images = io.load_images_from_dir(data_dir, suffix=suffix)
conditions = io.load_labels(epoch_file)
processed_data, labels = prepare_searchlight_mvpa_data(images,
conditions)
expected_searchlight_processed_data = np.load(
expected_dir / 'expected_searchlight_processed_data.npy')
for idx in range(len(processed_data)):
assert np.allclose(processed_data[idx],
expected_searchlight_processed_data[idx]), (
'raw data do not match in test_prepare_searchlight_mvpa_data')
assert np.array_equal(labels, expected_labels), \
'the labels do not match in test_prepare_searchlight_mvpa_data'
from brainiak.fcma.preprocessing import RandomType
images = io.load_images_from_dir(data_dir, suffix=suffix)
random_processed_data, _ = prepare_searchlight_mvpa_data(
images,
conditions,
random=RandomType.REPRODUCIBLE)
assert (len(random_processed_data)
== len(expected_searchlight_processed_data)), (
'numbers of epochs do not match in test_prepare_searchlight_mvpa_data')
images = io.load_images_from_dir(data_dir, suffix=suffix)
random_processed_data, _ = prepare_searchlight_mvpa_data(
images,
conditions,
random=RandomType.UNREPRODUCIBLE)
assert (len(random_processed_data)
== len(expected_searchlight_processed_data)), (
'numbers of epochs do not match in test_prepare_searchlight_mvpa_data')
def test_prepare_searchlight_mvpa_data():
images = io.load_images_from_dir(data_dir, suffix)
conditions = io.load_labels(epoch_file)
processed_data, labels = prepare_searchlight_mvpa_data(images, conditions)
expected_searchlight_processed_data = np.load(
expected_dir / 'expected_searchlight_processed_data.npy')
for idx in range(len(processed_data)):
assert np.allclose(processed_data[idx], expected_searchlight_processed_data[idx]), \
'raw data do not match in test_prepare_searchlight_mvpa_data'
assert np.array_equal(labels, expected_labels), \
'the labels do not match in test_prepare_searchlight_mvpa_data'
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
# all MPI processes read the mask; the mask file is small
mask_image = nib.load(mask_file)
mask = io.load_boolean_mask(mask_file)
data = None
labels = None
if MPI.COMM_WORLD.Get_rank()==0:
logger.info(
'mask size: %d' %
np.sum(mask)
)
images = io.load_images_from_dir(data_dir, suffix=suffix)
conditions = io.load_labels(epoch_file)
data, labels = prepare_searchlight_mvpa_data(images, conditions)
# setting the random argument produces random voxel selection results
# for non-parametric statistical analysis.
# There are three random options:
# RandomType.NORANDOM is the default
# RandomType.REPRODUCIBLE permutes the voxels in the same way every run
# 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]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
# all MPI processes read the mask; the mask file is small
mask_image = nib.load(mask_file)
mask = io.load_boolean_mask(mask_file)
data = None
labels = None
if MPI.COMM_WORLD.Get_rank()==0:
logger.info(
'mask size: %d' %
np.sum(mask)
)
images = io.load_images_from_dir(data_dir, suffix=suffix)
conditions = io.load_labels(epoch_file)
data, labels = prepare_searchlight_mvpa_data(images, conditions)
# setting the random argument produces random voxel selection results
# for non-parametric statistical analysis.
# There are three random options:
# RandomType.NORANDOM is the default
# RandomType.REPRODUCIBLE permutes the voxels in the same way every run
# 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]