def test_modelgen_sparse(): tempdir = mkdtemp() filename1 = os.path.join(tempdir, 'test1.nii') filename2 = os.path.join(tempdir, 'test2.nii') Nifti1Image(np.random.rand(10, 10, 10, 50), np.eye(4)).to_filename(filename1) Nifti1Image(np.random.rand(10, 10, 10, 50), np.eye(4)).to_filename(filename2) s = SpecifySparseModel() s.inputs.input_units = 'secs' s.inputs.functional_runs = [filename1, filename2] s.inputs.time_repetition = 6 info = [Bunch(conditions=['cond1'], onsets=[[0, 50, 100, 180]], durations=[[2]]), Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])] s.inputs.subject_info = info s.inputs.volumes_in_cluster = 1 s.inputs.time_acquisition = 2 s.inputs.high_pass_filter_cutoff = np.inf res = s.run() yield assert_equal, len(res.outputs.session_info), 2 yield assert_equal, len(res.outputs.session_info[0]['regress']), 1 yield assert_equal, len(res.outputs.session_info[0]['cond']), 0 s.inputs.stimuli_as_impulses = False res = s.run() yield assert_equal, res.outputs.session_info[0]['regress'][0]['val'][0], 1.0 s.inputs.model_hrf = True res = s.run() yield assert_almost_equal, res.outputs.session_info[0]['regress'][0]['val'][0], 0.016675298129743384 yield assert_equal, len(res.outputs.session_info[0]['regress']), 1 s.inputs.use_temporal_deriv = True res = s.run() yield assert_equal, len(res.outputs.session_info[0]['regress']), 2 yield assert_almost_equal, res.outputs.session_info[0]['regress'][0]['val'][0], 0.016675298129743384 yield assert_almost_equal, res.outputs.session_info[1]['regress'][1]['val'][5], 0.007671459162258378 rmtree(tempdir)
def test_modelgen_sparse(tmpdir): tempdir = str(tmpdir) filename1 = os.path.join(tempdir, 'test1.nii') filename2 = os.path.join(tempdir, 'test2.nii') Nifti1Image(np.random.rand(10, 10, 10, 50), np.eye(4)).to_filename(filename1) Nifti1Image(np.random.rand(10, 10, 10, 50), np.eye(4)).to_filename(filename2) s = SpecifySparseModel() s.inputs.input_units = 'secs' s.inputs.functional_runs = [filename1, filename2] s.inputs.time_repetition = 6 info = [Bunch(conditions=['cond1'], onsets=[[0, 50, 100, 180]], durations=[[2]]), Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])] s.inputs.subject_info = info s.inputs.volumes_in_cluster = 1 s.inputs.time_acquisition = 2 s.inputs.high_pass_filter_cutoff = np.inf res = s.run() assert len(res.outputs.session_info) == 2 assert len(res.outputs.session_info[0]['regress']) == 1 assert len(res.outputs.session_info[0]['cond']) == 0 s.inputs.stimuli_as_impulses = False res = s.run() assert res.outputs.session_info[0]['regress'][0]['val'][0] == 1.0 s.inputs.model_hrf = True res = s.run() npt.assert_almost_equal(res.outputs.session_info[0]['regress'][0]['val'][0], 0.016675298129743384) assert len(res.outputs.session_info[0]['regress']) == 1 s.inputs.use_temporal_deriv = True res = s.run() assert len(res.outputs.session_info[0]['regress']) == 2 npt.assert_almost_equal(res.outputs.session_info[0]['regress'][0]['val'][0], 0.016675298129743384) npt.assert_almost_equal(res.outputs.session_info[1]['regress'][1]['val'][5], 0.007671459162258378)