def test_hkernel():
""" test the hrf computation
"""
tr = 2.0
h = _hrf_kernel('spm', tr)
assert_almost_equal(h[0], spm_hrf(tr))
assert len(h) == 1
h = _hrf_kernel('spm + derivative', tr)
assert_almost_equal(h[1], spm_time_derivative(tr))
assert len(h) == 2
h = _hrf_kernel('spm + derivative + dispersion', tr)
assert_almost_equal(h[2], spm_dispersion_derivative(tr))
assert len(h) == 3
h = _hrf_kernel('glover', tr)
assert_almost_equal(h[0], glover_hrf(tr))
assert len(h) == 1
h = _hrf_kernel('glover + derivative', tr)
assert_almost_equal(h[1], glover_time_derivative(tr))
assert_almost_equal(h[0], glover_hrf(tr))
assert len(h) == 2
h = _hrf_kernel('fir', tr, fir_delays=np.arange(4))
assert len(h) == 4
for dh in h:
assert_almost_equal(dh.sum(), 1.)
h = _hrf_kernel(None, tr)
assert len(h) == 1
assert_almost_equal(h[0], np.hstack((1, np.zeros(49))))
def create_hrf(self, hrf_params=[1.0, 1.0, 0.0]):
"""
construct single or multiple HRFs
Parameters
----------
hrf_params : TYPE, optional
DESCRIPTION. The default is [1.0, 1.0, 0.0].
Returns
-------
TYPE
DESCRIPTION.
"""
hrf = np.array([
np.ones_like(hrf_params[1]) * hrf_params[0] *
spm_hrf(tr=self.stimulus.TR, oversampling=1,
time_length=40)[..., np.newaxis],
hrf_params[1] * spm_time_derivative(
tr=self.stimulus.TR, oversampling=1,
time_length=40)[..., np.newaxis],
hrf_params[2] * spm_dispersion_derivative(
tr=self.stimulus.TR, oversampling=1,
time_length=40)[..., np.newaxis]
]).sum(axis=0)
return hrf.T
def test_hkernel():
""" test the hrf computation
"""
tr = 2.0
h = _hrf_kernel('spm', tr)
assert_almost_equal(h[0], spm_hrf(tr))
assert len(h) == 1
h = _hrf_kernel('spm + derivative', tr)
assert_almost_equal(h[1], spm_time_derivative(tr))
assert len(h) == 2
h = _hrf_kernel('spm + derivative + dispersion', tr)
assert_almost_equal(h[2], spm_dispersion_derivative(tr))
assert len(h) == 3
h = _hrf_kernel('glover', tr)
assert_almost_equal(h[0], glover_hrf(tr))
assert len(h) == 1
h = _hrf_kernel('glover + derivative', tr)
assert_almost_equal(h[1], glover_time_derivative(tr))
assert_almost_equal(h[0], glover_hrf(tr))
assert len(h) == 2
h = _hrf_kernel('glover + derivative + dispersion', tr)
assert len(h) == 3
assert_almost_equal(h[2], glover_dispersion_derivative(tr))
assert_almost_equal(h[1], glover_time_derivative(tr))
assert_almost_equal(h[0], glover_hrf(tr))
h = _hrf_kernel('fir', tr, fir_delays=np.arange(4))
assert len(h) == 4
for dh in h:
assert_almost_equal(dh.sum(), 1.)
h = _hrf_kernel(None, tr)
assert len(h) == 1
assert_almost_equal(h[0], np.hstack((1, np.zeros(49))))
with pytest.raises(ValueError,
match="Could not process custom HRF model provided."):
_hrf_kernel(lambda x: np.ones(int(x)), tr)
_hrf_kernel([lambda x, y, z: x + y + z], tr)
_hrf_kernel([lambda x: np.ones(int(x))] * 2, tr)
h = _hrf_kernel(lambda tr, ov: np.ones(int(tr * ov)), tr)
assert len(h) == 1
assert_almost_equal(h[0], np.ones(100))
h = _hrf_kernel([lambda tr, ov: np.ones(int(tr * ov))], tr)
assert len(h) == 1
assert_almost_equal(h[0], np.ones(100))
with pytest.raises(ValueError, match="is not a known hrf model."):
_hrf_kernel("foo", tr)
def test_spm_hrf():
""" test that the spm_hrf is correctly normalized and has correct length
"""
h = spm_hrf(2.0)
assert_almost_equal(h.sum(), 1)
assert len(h) == 800