def test_prepare_fcma_data():
images = io.load_images_from_dir(data_dir, suffix=suffix)
mask = io.load_boolean_mask(mask_file)
conditions = io.load_labels(epoch_file)
raw_data, _, labels = prepare_fcma_data(images, conditions, mask)
expected_raw_data = np.load(expected_dir / 'expected_raw_data.npy')
assert len(raw_data) == len(expected_raw_data), \
'numbers of epochs do not match in test_prepare_fcma_data'
for idx in range(len(raw_data)):
assert np.allclose(raw_data[idx], expected_raw_data[idx]), \
'raw data do not match in test_prepare_fcma_data'
assert np.array_equal(labels, expected_labels), \
'the labels do not match in test_prepare_fcma_data'
from brainiak.fcma.preprocessing import RandomType
images = io.load_images_from_dir(data_dir, suffix=suffix)
random_raw_data, _, _ = prepare_fcma_data(images,
conditions,
mask,
random=RandomType.REPRODUCIBLE)
assert len(random_raw_data) == len(expected_raw_data), \
'numbers of epochs do not match in test_prepare_fcma_data'
images = io.load_images_from_dir(data_dir, suffix=suffix)
random_raw_data, _, _ = prepare_fcma_data(images,
conditions,
mask,
random=RandomType.UNREPRODUCIBLE)
assert len(random_raw_data) == len(expected_raw_data), \
'numbers of epochs do not match in test_prepare_fcma_data'
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_load_images_from_dir_data_shape(
in_dir: Path,
expected_image_data_shape: Sequence[int],
expected_n_subjects: int
) -> None:
for i, image in enumerate(io.load_images_from_dir(in_dir, "bet.nii.gz")):
assert image.get_data().shape == (64, 64, 26, 10)
assert i + 1 == expected_n_subjects
def test_load_images_from_dir_data_shape(
in_dir: Path,
expected_image_data_shape: Sequence[int],
expected_n_subjects: int
) -> None:
i = 0
for image in io.load_images_from_dir(in_dir, "bet.nii.gz"):
assert image.get_data().shape == (64, 64, 26, 10)
i += 1
assert i == expected_n_subjects
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'
def test_prepare_fcma_data():
images = io.load_images_from_dir(data_dir, suffix)
mask = io.load_boolean_mask(mask_file)
conditions = io.load_labels(epoch_file)
raw_data, _, labels = prepare_fcma_data(images, conditions, mask)
expected_raw_data = np.load(expected_dir / 'expected_raw_data.npy')
assert len(raw_data) == len(expected_raw_data), \
'numbers of epochs do not match in test_prepare_fcma_data'
for idx in range(len(raw_data)):
assert np.allclose(raw_data[idx], expected_raw_data[idx]), \
'raw data do not match in test_prepare_fcma_data'
assert np.array_equal(labels, expected_labels), \
'the labels do not match in test_prepare_fcma_data'
def test_prepare_mvpa_data():
images = io.load_images_from_dir(data_dir, suffix=suffix)
mask = io.load_boolean_mask(mask_file)
conditions = io.load_labels(epoch_file)
processed_data, labels = prepare_mvpa_data(images, conditions, mask)
expected_processed_data = np.load(expected_dir
/ 'expected_processed_data.npy')
assert len(processed_data) == len(expected_processed_data), \
'numbers of epochs do not match in test_prepare_mvpa_data'
for idx in range(len(processed_data)):
assert np.allclose(processed_data[idx],
expected_processed_data[idx]), (
'raw data do not match in test_prepare_mvpa_data')
assert np.array_equal(labels, expected_labels), \
'the labels do not match in test_prepare_mvpa_data'
def test_prepare_fcma_data():
images = io.load_images_from_dir(data_dir, suffix=suffix)
mask = io.load_boolean_mask(mask_file)
conditions = io.load_labels(epoch_file)
raw_data, _, labels = prepare_fcma_data(images, conditions, mask)
expected_raw_data = np.load(expected_dir / 'expected_raw_data.npy')
assert len(raw_data) == len(expected_raw_data), \
'numbers of epochs do not match in test_prepare_fcma_data'
for idx in range(len(raw_data)):
assert np.allclose(raw_data[idx], expected_raw_data[idx]), \
'raw data do not match in test_prepare_fcma_data'
assert np.array_equal(labels, expected_labels), \
'the labels do not match in test_prepare_fcma_data'
from brainiak.fcma.preprocessing import RandomType
images = io.load_images_from_dir(data_dir, suffix=suffix)
random_raw_data, _, _ = prepare_fcma_data(images, conditions, mask,
random=RandomType.REPRODUCIBLE)
assert len(random_raw_data) == len(expected_raw_data), \
'numbers of epochs do not match in test_prepare_fcma_data'
images = io.load_images_from_dir(data_dir, suffix=suffix)
random_raw_data, _, _ = prepare_fcma_data(images, conditions, mask,
random=RandomType.UNREPRODUCIBLE)
assert len(random_raw_data) == len(expected_raw_data), \
'numbers of epochs do not match in test_prepare_fcma_data'
# 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__)
"""
example running command in run_voxel_selection.sh
"""
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]
suffix = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
images = io.load_images_from_dir(data_dir, suffix=suffix)
mask = io.load_boolean_mask(mask_file)
conditions = io.load_labels(epoch_file)
raw_data, _, labels = prepare_fcma_data(images, conditions, mask)
# 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
# raw_data, _, labels = prepare_fcma_data(images, conditions, mask,
# random=RandomType.REPRODUCIBLE)
# python3 classification.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]
epoch_list = np.load(epoch_file)
num_subjects = len(epoch_list)
num_epochs_per_subj = epoch_list[0].shape[1]
images = io.load_images_from_dir(data_dir, extension)
mask = io.load_boolean_mask(mask_file)
conditions = io.load_labels(epoch_file)
raw_data, _, labels = prepare_fcma_data(images, conditions, mask)
example_of_aggregating_sim_matrix(raw_data, labels, num_subjects, num_epochs_per_subj)
example_of_cross_validation_with_detailed_info(raw_data, labels, num_subjects, num_epochs_per_subj)
example_of_cross_validation_using_model_selection(raw_data, labels, num_subjects, num_epochs_per_subj)
# test of two different components for correlation computation
# images = io.load_images_from_dir(data_dir, extension)
# mask2 = io.load_boolean_mask('face_scene/visual_top_mask.nii.gz')
# raw_data, raw_data2, labels = prepare_fcma_data(images, conditions, mask,
# mask2)
data_dir = sys.argv[1]
suffix = sys.argv[2]
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
