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)
