def test_fetch_spm_multimodal():
data_dir = os.path.join(tst.tmpdir, 'spm_multimodal_fmri')
os.mkdir(data_dir)
subject_dir = os.path.join(data_dir, 'sub001')
os.mkdir(subject_dir)
os.mkdir(os.path.join(subject_dir, 'fMRI'))
os.mkdir(os.path.join(subject_dir, 'sMRI'))
open(os.path.join(subject_dir, 'sMRI', 'smri.img'), 'a').close()
for session in [0, 1]:
open(
os.path.join(subject_dir, 'fMRI',
'trials_ses%i.mat' % (session + 1)), 'a').close()
dir_ = os.path.join(subject_dir, 'fMRI', 'Session%d' % (session + 1))
os.mkdir(dir_)
for i in range(390):
open(
os.path.join(dir_,
'fMETHODS-000%i-%i-01.img' % (session + 5, i)),
'a').close()
dataset = datasets.fetch_spm_multimodal_fmri(data_dir=tst.tmpdir)
assert_true(isinstance(dataset.anat, _basestring))
assert_true(isinstance(dataset.func1[0], _basestring))
assert_equal(len(dataset.func1), 390)
assert_true(isinstance(dataset.func2[0], _basestring))
assert_equal(len(dataset.func2), 390)
assert_equal(dataset.slice_order, 'descending')
assert_true(dataset.trials_ses1, _basestring)
assert_true(dataset.trials_ses2, _basestring)
def test_fetch_spm_multimodal(request_mocker, tmp_path):
data_dir = str(tmp_path / 'spm_multimodal_fmri')
os.mkdir(data_dir)
subject_dir = os.path.join(data_dir, 'sub001')
os.mkdir(subject_dir)
os.mkdir(os.path.join(subject_dir, 'fMRI'))
os.mkdir(os.path.join(subject_dir, 'sMRI'))
open(os.path.join(subject_dir, 'sMRI', 'smri.img'), 'a').close()
for session in [0, 1]:
open(os.path.join(subject_dir, 'fMRI',
'trials_ses%i.mat' % (session + 1)), 'a').close()
dir_ = os.path.join(subject_dir, 'fMRI', 'Session%d' % (session + 1))
os.mkdir(dir_)
for i in range(390):
open(os.path.join(dir_, 'fMETHODS-000%i-%i-01.img' %
(session + 5, i)), 'a').close()
dataset = datasets.fetch_spm_multimodal_fmri(data_dir=str(tmp_path))
assert isinstance(dataset.anat, _basestring)
assert isinstance(dataset.func1[0], _basestring)
assert len(dataset.func1) == 390
assert isinstance(dataset.func2[0], _basestring)
assert len(dataset.func2) == 390
assert dataset.slice_order == 'descending'
assert isinstance(dataset.trials_ses1, _basestring)
assert isinstance(dataset.trials_ses2, _basestring)
def test_fetch_spm_multimodal():
dataset = datasets.fetch_spm_multimodal_fmri(data_dir=tmpdir)
assert_true(isinstance(dataset.anat, _basestring))
assert_true(isinstance(dataset.func1[0], _basestring))
assert_equal(len(dataset.func1), 390)
assert_true(isinstance(dataset.func2[0], _basestring))
assert_equal(len(dataset.func2), 390)
assert_equal(dataset.slice_order, "descending")
assert_true(dataset.trials_ses1, _basestring)
assert_true(dataset.trials_ses2, _basestring)
def test_fetch_spm_multimodal():
dataset = datasets.fetch_spm_multimodal_fmri(data_dir=tmpdir)
assert_true(isinstance(dataset.anat, _basestring))
assert_true(isinstance(dataset.func1[0], _basestring))
assert_equal(len(dataset.func1), 390)
assert_true(isinstance(dataset.func2[0], _basestring))
assert_equal(len(dataset.func2), 390)
assert_equal(dataset.slice_order, 'descending')
assert_true(dataset.trials_ses1, _basestring)
assert_true(dataset.trials_ses2, _basestring)
Henson, R.N., Goshen-Gottstein, Y., Ganel, T., Otten, L.J., Quayle, A., Rugg, M.D. Electrophysiological and haemodynamic correlates of face perception, recognition and priming. Cereb Cortex. 2003 Jul;13(7):793-805. http://www.dx.doi.org/10.1093/cercor/13.7.793 This example takes a lot of time because the input are lists of 3D images sampled in different position (encoded by different) affine functions. """ print(__doc__) ######################################################################### # Fetch spm multimodal_faces data from nistats.datasets import fetch_spm_multimodal_fmri subject_data = fetch_spm_multimodal_fmri() ######################################################################### # Timing and design matrix parameter specification tr = 2. # repetition time, in seconds slice_time_ref = 0. # we will sample the design matrix at the beggining of each acquisition drift_model = 'Cosine' # We use a discrete cosin transform to model signal drifts. period_cut = 128. # The cutoff for the drift model is 1/128 Hz. hrf_model = 'spm + derivative' # The hemodunamic response finction is the SPM canonical one ######################################################################### # Resample the images. # # This is achieved by the concat_imgs function of Nilearn. from nilearn.image import concat_imgs, resample_img, mean_img fmri_img = [
Rugg, M.D. Electrophysiological and haemodynamic correlates of face perception, recognition and priming. Cereb Cortex. 2003 Jul;13(7):793-805. http://www.dx.doi.org/10.1093/cercor/13.7.793 This example takes a lot of time because the input are lists of 3D images sampled in different position (encoded by different) affine functions. """ print(__doc__) ######################################################################### # Fetch spm multimodal_faces data from nistats.datasets import fetch_spm_multimodal_fmri subject_data = fetch_spm_multimodal_fmri() ######################################################################### # Timing and design matrix parameter specification tr = 2. # repetition time, in seconds slice_time_ref = 0. # we will sample the design matrix at the beggining of each acquisition drift_model = 'Cosine' # We use a discrete cosin transform to model signal drifts. period_cut = 128. # The cutoff for the drift model is 1/128 Hz. hrf_model = 'spm + derivative' # The hemodunamic response finction is the SPM canonical one ######################################################################### # Resample the images. # # This is achieved by the concat_imgs function of Nilearn. from nilearn.image import concat_imgs, resample_img, mean_img fmri_img = [concat_imgs(subject_data.func1, auto_resample=True),
