def test__combine_t2s():
"""
Test tedana.combine._combine_t2s
"""
np.random.seed(0)
n_voxels, n_echos, n_trs = 20, 3, 10
data = np.random.random((n_voxels, n_echos, n_trs))
tes = np.array([[10, 20, 30]]) # 1 x E
# Voxel- and volume-wise T2* estimates
t2s = np.random.random((n_voxels, n_trs, 1)) # M x T x 1
comb = combine._combine_t2s(data, tes, t2s)
assert comb.shape == (n_voxels, n_trs)
# Voxel-wise T2* estimates
t2s = np.random.random((n_voxels, 1)) # M x 1
comb = combine._combine_t2s(data, tes, t2s)
assert comb.shape == (n_voxels, n_trs)
def test__apply_t2s_floor():
"""
_apply_t2s_floor applies a floor to T2* values to prevent a ZeroDivisionError during
optimal combination.
"""
n_voxels, n_echos, n_trs = 100, 5, 25
echo_times = np.array([2, 23, 54, 75, 96])
me_data = np.random.random((n_voxels, n_echos, n_trs))
t2s = np.random.random((n_voxels)) * 1000
t2s[t2s < 1] = 1 # Crop at 1 ms to be safe
t2s[0] = 0.001
# First establish a failure
with pytest.raises(ZeroDivisionError):
_ = combine._combine_t2s(me_data, echo_times[None, :], t2s[:, None])
# Now correct the T2* map and get a successful result.
t2s_corrected = me._apply_t2s_floor(t2s, echo_times)
assert t2s_corrected[0] != t2s[0] # First value should be corrected
assert np.array_equal(t2s_corrected[1:],
t2s[1:]) # No other values should be corrected
combined = combine._combine_t2s(me_data, echo_times[None, :],
t2s_corrected[:, None])
assert np.all(combined != 0)