def test_convolve(collection):
rt = collection.variables['RT']
transform.Convolve(collection, 'RT', output='reaction_time')
rt_conv = collection.variables['reaction_time']
assert rt_conv.values.shape[0] == \
rt.get_duration() * collection.sampling_rate
transform.ToDense(collection, 'RT', output='rt_dense')
transform.Convolve(collection, 'rt_dense', output='dense_convolved')
dense_conv = collection.variables['reaction_time']
assert dense_conv.values.shape[0] == \
rt.get_duration() * collection.sampling_rate
# Test adapative oversampling computation
with mock.patch('bids.analysis.transformations.compute.hrf') as mocked:
transform.Convolve(collection, 'RT', output='rt_mock')
mocked.compute_regressor.assert_called_with(
mock.ANY, 'spm', mock.ANY, fir_delays=None, min_onset=0,
oversampling=1.0)
with mock.patch('bids.analysis.transformations.compute.hrf') as mocked:
transform.Convolve(collection, 'rt_dense', output='rt_mock')
mocked.compute_regressor.assert_called_with(
mock.ANY, 'spm', mock.ANY, fir_delays=None, min_onset=0,
oversampling=3.0)
with mock.patch('bids.analysis.transformations.compute.hrf') as mocked:
collection.sampling_rate = 0.5
transform.Convolve(collection, 'RT', output='rt_mock')
mocked.compute_regressor.assert_called_with(
mock.ANY, 'spm', mock.ANY, fir_delays=None, min_onset=0,
oversampling=2.0)
def test_convolve(collection):
rt = collection.variables['RT']
transform.Convolve(collection, 'RT', output='reaction_time')
rt_conv = collection.variables['reaction_time']
assert rt_conv.values.shape[0] == \
rt.get_duration() * collection.sampling_rate
transform.ToDense(collection, 'RT', output='rt_dense')
transform.Convolve(collection, 'rt_dense', output='dense_convolved')
dense_conv = collection.variables['reaction_time']
assert dense_conv.values.shape[0] == \
rt.get_duration() * collection.sampling_rate
# Test adapative oversampling computation
# Events are 3s duration events every 4s, so resolution demanded by the data is 1Hz
# To resolve 1Hz frequencies, we must sample at >=2Hz
args = (mock.ANY, 'spm', mock.ANY)
kwargs = dict(fir_delays=None, min_onset=0)
with mock.patch('bids.analysis.transformations.compute.hrf') as mocked:
# Sampling rate is 10Hz, no oversampling needed
transform.Convolve(collection, 'RT', output='rt_mock')
mocked.compute_regressor.assert_called_with(*args,
oversampling=1.0,
**kwargs)
with mock.patch('bids.analysis.transformations.compute.hrf') as mocked:
# Sampling rate is 10Hz, no oversampling needed
transform.Convolve(collection, 'rt_dense', output='rt_mock')
mocked.compute_regressor.assert_called_with(*args,
oversampling=1.0,
**kwargs)
with mock.patch('bids.analysis.transformations.compute.hrf') as mocked:
# Slow sampling rate, oversample (4x) to 2Hz
collection.sampling_rate = 0.5
transform.Convolve(collection, 'RT', output='rt_mock')
mocked.compute_regressor.assert_called_with(*args,
oversampling=4.0,
**kwargs)
with mock.patch('bids.analysis.transformations.compute.hrf') as mocked:
# Dense variable is already sampled at 10Hz, no oversampling needed
collection.sampling_rate = 0.5
transform.Convolve(collection, 'rt_dense', output='rt_mock')
mocked.compute_regressor.assert_called_with(*args,
oversampling=1.0,
**kwargs)
with mock.patch('bids.analysis.transformations.compute.hrf') as mocked:
# Onset requires 10Hz resolution, oversample (2x) to 20Hz
collection.sampling_rate = 10
collection['RT'].onset[0] += 0.1
transform.Convolve(collection, 'RT', output='rt_mock')
mocked.compute_regressor.assert_called_with(*args,
oversampling=2.0,
**kwargs)
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_resample(collection):
coll = collection.clone()
transform.ToDense(coll, 'parametric gain', output='pg_dense')
pg = coll.variables['pg_dense']
old_shape = pg.values.shape
old_auc = np.trapz(np.abs(pg.values.values.squeeze()), dx=0.1)
transform.Resample(coll, 'pg_dense', 1)
pg = coll.variables['pg_dense']
new_shape = pg.values.shape
# Spacing (dx) is 10* larger when downsampled fro 10hz to 1hz
new_auc = np.trapz(np.abs(pg.values.values.squeeze()), dx=1)
# Shape from 10hz to 1hz
assert new_shape[0] == old_shape[0] / 10
# Assert that the auc is more or less the same (not exact, rounding error)
# Values are around 0.25
assert np.allclose(old_auc, new_auc, rtol=0.05)