def test_shore_metrics():
fetch_taiwan_ntu_dsi()
img, gtab = read_taiwan_ntu_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(gtab, mevals, S0=100, angles=angl,
fractions=[50, 50], snr=None)
S = S / S[0, None].astype(np.float)
radial_order = 8
zeta = 800
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = asm.fit(S)
c_shore= asmfit.shore_coeff
cmat = SHOREmatrix(radial_order, zeta, gtab)
S_reconst = np.dot(cmat, c_shore)
nmse_signal = np.sqrt(np.sum((S - S_reconst) ** 2)) / (S.sum())
assert_almost_equal(nmse_signal, 0.0, 4)
mevecs2 = np.zeros((2, 3, 3))
angl = np.array(angl)
for i in range(2):
mevecs2[i] = all_tensor_evecs(sticks[i]).T
sphere = get_sphere('symmetric724')
v = sphere.vertices
radius = 10e-3
pdf_shore = asmfit.pdf(v * radius)
pdf_mt = multi_tensor_pdf(v * radius, [.5, .5], mevals=mevals, mevecs=mevecs2)
nmse_pdf = np.sqrt(np.sum((pdf_mt - pdf_shore) ** 2)) / (pdf_mt.sum())
assert_almost_equal(nmse_pdf, 0.0, 2)
rtop_shore_signal = asmfit.rtop_signal()
rtop_shore_pdf = asmfit.rtop_pdf()
assert_almost_equal(rtop_shore_signal, rtop_shore_pdf, 9)
rtop_mt = multi_tensor_rtop([.5, .5], mevals=mevals)
assert_equal(rtop_mt/rtop_shore_signal < 1.12 and rtop_mt/rtop_shore_signal > 0.9 , True)
msd_mt = multi_tensor_msd([.5, .5], mevals=mevals)
msd_shore = asmfit.msd()
assert_equal(msd_mt/msd_shore < 1.05 and msd_mt/msd_shore > 0.95 , True)
def test_shore_metrics():
gtab = get_gtab_taiwan_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(gtab, mevals, S0=100.0, angles=angl,
fractions=[50, 50], snr=None)
# test shore_indices
n = 7
l = 6
m = -4
radial_order, c = shore_order(n, l, m)
n2, l2, m2 = shore_indices(radial_order, c)
assert_equal(n, n2)
assert_equal(l, l2)
assert_equal(m, m2)
radial_order = 6
c = 41
n, l, m = shore_indices(radial_order, c)
radial_order2, c2 = shore_order(n, l, m)
assert_equal(radial_order, radial_order2)
assert_equal(c, c2)
# since we are testing without noise we can use higher order and lower lambdas, with respect to the default.
radial_order = 8
zeta = 700
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab, radial_order=radial_order,
zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = asm.fit(S)
c_shore = asmfit.shore_coeff
cmat = shore_matrix(radial_order, zeta, gtab)
S_reconst = np.dot(cmat, c_shore)
# test the signal reconstruction
S = S / S[0]
nmse_signal = np.sqrt(np.sum((S - S_reconst) ** 2)) / (S.sum())
assert_almost_equal(nmse_signal, 0.0, 4)
# test if the analytical integral of the pdf is equal to one
integral = 0
for n in range(int((radial_order)/2 +1)):
integral += c_shore[n] * (np.pi**(-1.5) * zeta **(-1.5) * genlaguerre(n,0.5)(0)) ** 0.5
assert_almost_equal(integral, 1.0, 10)
# test if the integral of the pdf calculated on a discrete grid is equal to one
pdf_discrete = asmfit.pdf_grid(17, 40e-3)
integral = pdf_discrete.sum()
assert_almost_equal(integral, 1.0, 1)
# compare the shore pdf with the ground truth multi_tensor pdf
sphere = get_sphere('symmetric724')
v = sphere.vertices
radius = 10e-3
pdf_shore = asmfit.pdf(v * radius)
pdf_mt = multi_tensor_pdf(v * radius, mevals=mevals,
angles=angl, fractions= [50, 50])
nmse_pdf = np.sqrt(np.sum((pdf_mt - pdf_shore) ** 2)) / (pdf_mt.sum())
assert_almost_equal(nmse_pdf, 0.0, 2)
# compare the shore rtop with the ground truth multi_tensor rtop
rtop_shore_signal = asmfit.rtop_signal()
rtop_shore_pdf = asmfit.rtop_pdf()
assert_almost_equal(rtop_shore_signal, rtop_shore_pdf, 9)
rtop_mt = multi_tensor_rtop([.5, .5], mevals=mevals)
assert_equal(rtop_mt / rtop_shore_signal <1.10 and rtop_mt / rtop_shore_signal > 0.95, True)
# compare the shore msd with the ground truth multi_tensor msd
msd_mt = multi_tensor_msd([.5, .5], mevals=mevals)
msd_shore = asmfit.msd()
assert_equal(msd_mt / msd_shore < 1.05 and msd_mt / msd_shore > 0.95, True)
def test_shore_metrics():
gtab = get_gtab_taiwan_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, _ = multi_tensor(gtab, mevals, S0=100.0, angles=angl,
fractions=[50, 50], snr=None)
# test shore_indices
n = 7
l = 6
m = -4
radial_order, c = shore_order(n, l, m)
n2, l2, m2 = shore_indices(radial_order, c)
npt.assert_equal(n, n2)
npt.assert_equal(l, l2)
npt.assert_equal(m, m2)
radial_order = 6
c = 41
n, l, m = shore_indices(radial_order, c)
radial_order2, c2 = shore_order(n, l, m)
npt.assert_equal(radial_order, radial_order2)
npt.assert_equal(c, c2)
npt.assert_raises(ValueError, shore_indices, 6, 100)
npt.assert_raises(ValueError, shore_order, m, n, l)
# since we are testing without noise we can use higher order and lower
# lambdas, with respect to the default.
radial_order = 8
zeta = 700
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab, radial_order=radial_order,
zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = asm.fit(S)
c_shore = asmfit.shore_coeff
cmat = shore_matrix(radial_order, zeta, gtab)
S_reconst = np.dot(cmat, c_shore)
# test the signal reconstruction
S = S / S[0]
nmse_signal = np.sqrt(np.sum((S - S_reconst) ** 2)) / (S.sum())
npt.assert_almost_equal(nmse_signal, 0.0, 4)
# test if the analytical integral of the pdf is equal to one
integral = 0
for n in range(int((radial_order)/2 + 1)):
integral += c_shore[n] * (np.pi**(-1.5) * zeta ** (-1.5) *
genlaguerre(n, 0.5)(0)) ** 0.5
npt.assert_almost_equal(integral, 1.0, 10)
# test if the integral of the pdf calculated on a discrete grid is
# equal to one
pdf_discrete = asmfit.pdf_grid(17, 40e-3)
integral = pdf_discrete.sum()
npt.assert_almost_equal(integral, 1.0, 1)
# compare the shore pdf with the ground truth multi_tensor pdf
sphere = get_sphere('symmetric724')
v = sphere.vertices
radius = 10e-3
pdf_shore = asmfit.pdf(v * radius)
pdf_mt = multi_tensor_pdf(v * radius, mevals=mevals,
angles=angl, fractions=[50, 50])
nmse_pdf = np.sqrt(np.sum((pdf_mt - pdf_shore) ** 2)) / (pdf_mt.sum())
npt.assert_almost_equal(nmse_pdf, 0.0, 2)
# compare the shore rtop with the ground truth multi_tensor rtop
rtop_shore_signal = asmfit.rtop_signal()
rtop_shore_pdf = asmfit.rtop_pdf()
npt.assert_almost_equal(rtop_shore_signal, rtop_shore_pdf, 9)
rtop_mt = multi_tensor_rtop([.5, .5], mevals=mevals)
npt.assert_equal(rtop_mt / rtop_shore_signal < 1.10 and
rtop_mt / rtop_shore_signal > 0.95, True)
# compare the shore msd with the ground truth multi_tensor msd
msd_mt = multi_tensor_msd([.5, .5], mevals=mevals)
msd_shore = asmfit.msd()
npt.assert_equal(msd_mt / msd_shore < 1.05 and msd_mt / msd_shore > 0.95,
True)