def test_design_matrix():
ub = unique_bvals(bvals_3s)
D = msdki.design_matrix(ub)
Dgt = np.ones((4, 3))
Dgt[:, 0] = -ub
Dgt[:, 1] = 1.0 / 6 * ub**2
assert_array_almost_equal(D, Dgt)
def test_design_matrix():
ub = unique_bvals(bvals_3s)
D = msdki.design_matrix(ub)
Dgt = np.ones((4, 3))
Dgt[:, 0] = -ub
Dgt[:, 1] = 1.0/6 * ub ** 2
assert_array_almost_equal(D, Dgt)
def test_msdki_statistics():
# tests if MD and MK are equal to expected values of a spherical
# tensors
# Multi-tensors
ub = unique_bvals(bvals_3s)
design_matrix = msdki.design_matrix(ub)
msignal, ng = msdki.mean_signal_bvalue(DWI, gtab_3s, bmag=None)
params = msdki.wls_fit_msdki(design_matrix, msignal, ng)
assert_array_almost_equal(params[..., 1], MKgt_multi)
assert_array_almost_equal(params[..., 0], MDgt_multi)
mdkiM = msdki.MeanDiffusionKurtosisModel(gtab_3s)
mdkiF = mdkiM.fit(DWI)
mk = mdkiF.msk
md = mdkiF.msd
assert_array_almost_equal(MKgt_multi, mk)
assert_array_almost_equal(MDgt_multi, md)
# Single-tensors
mdkiF = mdkiM.fit(signal_sph)
mk = mdkiF.msk
md = mdkiF.msd
assert_array_almost_equal(MKgt, mk)
assert_array_almost_equal(MDgt, md)
# Test with given mask
mask = np.ones(DWI.shape[:-1])
v = (0, 0, 0)
mask[1, 1, 1] = 0
mdkiF = mdkiM.fit(DWI, mask=mask)
mk = mdkiF.msk
md = mdkiF.msd
assert_array_almost_equal(MKgt_multi, mk)
assert_array_almost_equal(MDgt_multi, md)
assert_array_almost_equal(MKgt_multi[v], mdkiF[v].msk) # tuple case
assert_array_almost_equal(MDgt_multi[v], mdkiF[v].msd) # tuple case
assert_array_almost_equal(MKgt_multi[0], mdkiF[0].msk) # not tuple case
assert_array_almost_equal(MDgt_multi[0], mdkiF[0].msd) # not tuple case
# Test returned S0
mdkiM = msdki.MeanDiffusionKurtosisModel(gtab_3s, return_S0_hat=True)
mdkiF = mdkiM.fit(DWI)
assert_array_almost_equal(S0gt_multi, mdkiF.S0_hat)
assert_array_almost_equal(MKgt_multi[v], mdkiF[v].msk)
def test_msdki_statistics():
# tests if MD and MK are equal to expected values of a spherical
# tensors
# Multi-tensors
ub = unique_bvals(bvals_3s)
design_matrix = msdki.design_matrix(ub)
msignal, ng = msdki.mean_signal_bvalue(DWI, gtab_3s, bmag=None)
params = msdki.wls_fit_msdki(design_matrix, msignal, ng)
assert_array_almost_equal(params[..., 1], MKgt_multi)
assert_array_almost_equal(params[..., 0], MDgt_multi)
mdkiM = msdki.MeanDiffusionKurtosisModel(gtab_3s)
mdkiF = mdkiM.fit(DWI)
mk = mdkiF.msk
md = mdkiF.msd
assert_array_almost_equal(MKgt_multi, mk)
assert_array_almost_equal(MDgt_multi, md)
# Single-tensors
mdkiF = mdkiM.fit(signal_sph)
mk = mdkiF.msk
md = mdkiF.msd
assert_array_almost_equal(MKgt, mk)
assert_array_almost_equal(MDgt, md)
# Test with given mask
mask = np.ones(DWI.shape[:-1])
v = (0, 0, 0)
mask[1, 1, 1] = 0
mdkiF = mdkiM.fit(DWI, mask=mask)
mk = mdkiF.msk
md = mdkiF.msd
assert_array_almost_equal(MKgt_multi, mk)
assert_array_almost_equal(MDgt_multi, md)
assert_array_almost_equal(MKgt_multi[v], mdkiF[v].msk) # tuple case
assert_array_almost_equal(MDgt_multi[v], mdkiF[v].msd) # tuple case
assert_array_almost_equal(MKgt_multi[0], mdkiF[0].msk) # not tuple case
assert_array_almost_equal(MDgt_multi[0], mdkiF[0].msd) # not tuple case
# Test returned S0
mdkiM = msdki.MeanDiffusionKurtosisModel(gtab_3s, return_S0_hat=True)
mdkiF = mdkiM.fit(DWI)
assert_array_almost_equal(S0gt_multi, mdkiF.S0_hat)
assert_array_almost_equal(MKgt_multi[v], mdkiF[v].msk)