def generate_DEV(name='test', sr=20, duration=480):
n = duration * sr
values = np.random.normal(size=n)
ent_names = ['task', 'run', 'session', 'subject']
entities = {e: uuid.uuid4().hex for e in ent_names}
image = uuid.uuid4().hex + '.nii.gz'
run_info = RunInfo(entities, duration, 2, image)
return DenseRunVariable('test', values, run_info, 'dummy', sr)
def sparse_run_variable_with_missing_values():
data = pd.DataFrame({
'onset': [2, 5, 11, 17],
'duration': [1.2, 1.6, 0.8, 2],
'amplitude': [1, 1, np.nan, 1]
})
run_info = [RunInfo({'subject': '01'}, 20, 2, 'dummy.nii.gz')]
var = SparseRunVariable('var', data, run_info, 'events')
return BIDSRunVariableCollection([var])
def test_Lag():
var = DenseRunVariable(
name="rot_x",
values=np.arange(5., 20.),
run_info=RunInfo({}, 15, 1, "none", 15),
source='regressors',
sampling_rate=1
)
coll = BIDSRunVariableCollection([var], sampling_rate=1)
# Forward shift
transform.Lag(coll, "rot_x", output="d_rot_x")
d_rot_x = coll["d_rot_x"].values.values
assert np.isclose(d_rot_x[0, 0], 5.)
assert np.allclose(d_rot_x[1:, 0], np.arange(5., 19.))
# Backward shift
transform.Lag(coll, "rot_x", output="d_rot_x", shift=-1)
d_rot_x = coll["d_rot_x"].values.values
assert np.isclose(d_rot_x[-1, 0], 19.)
assert np.allclose(d_rot_x[:-1, 0], np.arange(6., 20.))
# Half shift; don't know why you'd want to do it, but you can
transform.Lag(coll, "rot_x", output="half_shift", shift=0.5, order=1)
half_shift = coll["half_shift"].values.values
assert np.isclose(half_shift[0, 0], 5.)
assert np.allclose(half_shift[1:, 0], np.arange(5.5, 19.5))
# Constant mode
transform.Lag(coll, "rot_x", output="d_rot_x", mode="constant")
d_rot_x = coll["d_rot_x"].values.values
assert np.isclose(d_rot_x[0, 0], 0.)
assert np.allclose(d_rot_x[1:, 0], np.arange(5., 19.))
# Reflect mode
transform.Lag(coll, "rot_x", output="d_rot_x", mode="reflect")
d_rot_x = coll["d_rot_x"].values.values
assert np.isclose(d_rot_x[0, 0], 5.)
assert np.allclose(d_rot_x[1:, 0], np.arange(5., 19.))
# Forward shift -> Backward difference
transform.Lag(coll, "rot_x", output="d_rot_x", difference=True)
d_rot_x = coll["d_rot_x"].values.values
assert np.isclose(d_rot_x[0, 0], 0.)
assert np.allclose(d_rot_x[1:, 0], 1.)
# Backward shift -> Forward difference
transform.Lag(coll, "rot_x", output="d_rot_x", shift=-1, difference=True)
d_rot_x = coll["d_rot_x"].values.values
assert np.isclose(d_rot_x[-1, 0], 0.)
assert np.allclose(d_rot_x[:-1, 0], 1.)
def test_convolve_impulse():
# Smoke test impulse convolution
data = pd.DataFrame({
'onset': [10, 20],
'duration': [0, 0],
'amplitude': [1, 1]
})
run_info = [RunInfo({'subject': '01'}, 20, 2, 'dummy.nii.gz', 10)]
var = SparseRunVariable(
name='var', data=data, run_info=run_info, source='events')
coll = BIDSRunVariableCollection([var])
transform.ToDense(coll, 'var', output='var_dense')
transform.Convolve(coll, 'var', output='var_hrf')
transform.Convolve(coll, 'var_dense', output='var_dense_hrf')
def test_run_variable_collection_get_sampling_rate(run_coll):
coll = run_coll.clone()
assert coll._get_sampling_rate(None) == 10
coll.variables['RT'].run_info[0] = RunInfo({}, 200, 10, None, 20)
with pytest.raises(ValueError) as exc:
coll._get_sampling_rate('TR')
assert str(exc.value).startswith('Non-unique')
assert coll._get_sampling_rate('highest') == 10
coll.variables['RT1'] = coll.variables['RT'].to_dense(5.)
coll.variables['RT2'] = coll.variables['RT'].to_dense(12.)
assert coll._get_sampling_rate('highest') == 12.
assert coll._get_sampling_rate(20) == 20
with pytest.raises(ValueError) as exc:
coll._get_sampling_rate('BLARGH')
assert str(exc.value).startswith('Invalid')
