def test_cartesian_normalization(radial_order=4, time_order=2):
gtab_4d = generate_gtab4D()
l1, l2, l3 = [0.0015, 0.0003, 0.0003]
S = generate_signal_crossing(gtab_4d, l1, l2, l3)
qtdmri_mod_aniso = qtdmri.QtdmriModel(gtab_4d,
radial_order=radial_order,
time_order=time_order,
cartesian=True,
normalization=False)
qtdmri_mod_aniso_norm = qtdmri.QtdmriModel(gtab_4d,
radial_order=radial_order,
time_order=time_order,
cartesian=True,
normalization=True)
qtdmri_fit_aniso = qtdmri_mod_aniso.fit(S)
qtdmri_fit_aniso_norm = qtdmri_mod_aniso_norm.fit(S)
assert_array_almost_equal(qtdmri_fit_aniso.fitted_signal(),
qtdmri_fit_aniso_norm.fitted_signal())
rt_grid = qtdmri.create_rt_space_grid(5, 20e-3, 5, 0.02, .05)
pdf_aniso = qtdmri_fit_aniso.pdf(rt_grid)
pdf_aniso_norm = qtdmri_fit_aniso_norm.pdf(rt_grid)
assert_array_almost_equal(pdf_aniso / pdf_aniso.max(),
pdf_aniso_norm / pdf_aniso.max())
norm_laplacian = qtdmri_fit_aniso.norm_of_laplacian_signal()
norm_laplacian_norm = qtdmri_fit_aniso_norm.norm_of_laplacian_signal()
assert_array_almost_equal(norm_laplacian / norm_laplacian,
norm_laplacian_norm / norm_laplacian)
def test_spherical_normalization(radial_order=4, time_order=2):
gtab_4d = generate_gtab4D()
l1, l2, l3 = [0.0015, 0.0003, 0.0003]
S = generate_signal_crossing(gtab_4d, l1, l2, l3)
qtdmri_mod_aniso = qtdmri.QtdmriModel(gtab_4d, radial_order=radial_order,
time_order=time_order,
cartesian=False,
normalization=False)
qtdmri_mod_aniso_norm = qtdmri.QtdmriModel(gtab_4d,
radial_order=radial_order,
time_order=time_order,
cartesian=False,
normalization=True)
qtdmri_fit = qtdmri_mod_aniso.fit(S)
qtdmri_fit_norm = qtdmri_mod_aniso_norm.fit(S)
assert_array_almost_equal(qtdmri_fit.fitted_signal(),
qtdmri_fit_norm.fitted_signal())
rt_grid = qtdmri.create_rt_space_grid(5, 20e-3, 5, 0.02, .05)
pdf = qtdmri_fit.pdf(rt_grid)
pdf_norm = qtdmri_fit_norm.pdf(rt_grid)
assert_array_almost_equal(pdf / pdf.max(),
pdf_norm / pdf.max())
norm_laplacian = qtdmri_fit.norm_of_laplacian_signal()
norm_laplacian_norm = qtdmri_fit_norm.norm_of_laplacian_signal()
assert_array_almost_equal(norm_laplacian / norm_laplacian,
norm_laplacian_norm / norm_laplacian)
def test_anisotropic_isotropic_equivalence(radial_order=4, time_order=2):
# generate qt-scheme and arbitary synthetic crossing data.
gtab_4d = generate_gtab4D()
l1, l2, l3 = [0.0015, 0.0003, 0.0003]
S = generate_signal_crossing(gtab_4d, l1, l2, l3)
# initialize both cartesian and spherical models without any kind of
# regularization
qtdmri_mod_aniso = qtdmri.QtdmriModel(gtab_4d,
radial_order=radial_order,
time_order=time_order,
cartesian=True,
anisotropic_scaling=False)
qtdmri_mod_iso = qtdmri.QtdmriModel(gtab_4d,
radial_order=radial_order,
time_order=time_order,
cartesian=False,
anisotropic_scaling=False)
# both implementations fit the same signal
qtdmri_fit_cart = qtdmri_mod_aniso.fit(S)
qtdmri_fit_sphere = qtdmri_mod_iso.fit(S)
# same signal fit
assert_array_almost_equal(qtdmri_fit_cart.fitted_signal(),
qtdmri_fit_sphere.fitted_signal())
# same PDF reconstruction
rt_grid = qtdmri.create_rt_space_grid(5, 20e-3, 5, 0.02, .05)
pdf_aniso = qtdmri_fit_cart.pdf(rt_grid)
pdf_iso = qtdmri_fit_sphere.pdf(rt_grid)
assert_array_almost_equal(pdf_aniso / pdf_aniso.max(),
pdf_iso / pdf_aniso.max())
# same norm of the laplacian
norm_laplacian_aniso = qtdmri_fit_cart.norm_of_laplacian_signal()
norm_laplacian_iso = qtdmri_fit_sphere.norm_of_laplacian_signal()
assert_almost_equal(norm_laplacian_aniso / norm_laplacian_aniso,
norm_laplacian_iso / norm_laplacian_aniso)
# all q-space index is the same for arbitrary tau
tau = 0.02
assert_almost_equal(qtdmri_fit_cart.rtop(tau), qtdmri_fit_sphere.rtop(tau))
assert_almost_equal(qtdmri_fit_cart.rtap(tau), qtdmri_fit_sphere.rtap(tau))
assert_almost_equal(qtdmri_fit_cart.rtpp(tau), qtdmri_fit_sphere.rtpp(tau))
assert_almost_equal(qtdmri_fit_cart.msd(tau), qtdmri_fit_sphere.msd(tau))
assert_almost_equal(qtdmri_fit_cart.qiv(tau), qtdmri_fit_sphere.qiv(tau))
# ODF estimation is the same
sphere = get_sphere()
assert_array_almost_equal(qtdmri_fit_cart.odf(sphere, tau, s=0),
qtdmri_fit_sphere.odf(sphere, tau, s=0))
def test_anisotropic_isotropic_equivalence(radial_order=4, time_order=2):
# generate qt-scheme and arbitary synthetic crossing data.
gtab_4d = generate_gtab4D()
l1, l2, l3 = [0.0015, 0.0003, 0.0003]
S = generate_signal_crossing(gtab_4d, l1, l2, l3)
# initialize both cartesian and spherical models without any kind of
# regularization
qtdmri_mod_aniso = qtdmri.QtdmriModel(gtab_4d, radial_order=radial_order,
time_order=time_order,
cartesian=True,
anisotropic_scaling=False)
qtdmri_mod_iso = qtdmri.QtdmriModel(gtab_4d, radial_order=radial_order,
time_order=time_order,
cartesian=False,
anisotropic_scaling=False)
# both implementations fit the same signal
qtdmri_fit_cart = qtdmri_mod_aniso.fit(S)
qtdmri_fit_sphere = qtdmri_mod_iso.fit(S)
# same signal fit
assert_array_almost_equal(qtdmri_fit_cart.fitted_signal(),
qtdmri_fit_sphere.fitted_signal())
# same PDF reconstruction
rt_grid = qtdmri.create_rt_space_grid(5, 20e-3, 5, 0.02, .05)
pdf_aniso = qtdmri_fit_cart.pdf(rt_grid)
pdf_iso = qtdmri_fit_sphere.pdf(rt_grid)
assert_array_almost_equal(pdf_aniso / pdf_aniso.max(),
pdf_iso / pdf_aniso.max())
# same norm of the laplacian
norm_laplacian_aniso = qtdmri_fit_cart.norm_of_laplacian_signal()
norm_laplacian_iso = qtdmri_fit_sphere.norm_of_laplacian_signal()
assert_almost_equal(norm_laplacian_aniso / norm_laplacian_aniso,
norm_laplacian_iso / norm_laplacian_aniso)
# all q-space index is the same for arbitrary tau
tau = 0.02
assert_almost_equal(qtdmri_fit_cart.rtop(tau), qtdmri_fit_sphere.rtop(tau))
assert_almost_equal(qtdmri_fit_cart.rtap(tau), qtdmri_fit_sphere.rtap(tau))
assert_almost_equal(qtdmri_fit_cart.rtpp(tau), qtdmri_fit_sphere.rtpp(tau))
assert_almost_equal(qtdmri_fit_cart.msd(tau), qtdmri_fit_sphere.msd(tau))
assert_almost_equal(qtdmri_fit_cart.qiv(tau), qtdmri_fit_sphere.qiv(tau))
# ODF estimation is the same
sphere = get_sphere()
assert_array_almost_equal(qtdmri_fit_cart.odf(sphere, tau, s=0),
qtdmri_fit_sphere.odf(sphere, tau, s=0))
def test_spherical_normalization(radial_order=4, time_order=2):
gtab_4d = generate_gtab4D()
l1, l2, l3 = [0.0015, 0.0003, 0.0003]
S = generate_signal_crossing(gtab_4d, l1, l2, l3)
qtdmri_mod_aniso = qtdmri.QtdmriModel(gtab_4d,
radial_order=radial_order,
time_order=time_order,
cartesian=False,
normalization=False)
qtdmri_mod_aniso_norm = qtdmri.QtdmriModel(gtab_4d,
radial_order=radial_order,
time_order=time_order,
cartesian=False,
normalization=True)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
qtdmri_fit = qtdmri_mod_aniso.fit(S)
qtdmri_fit_norm = qtdmri_mod_aniso_norm.fit(S)
assert_array_almost_equal(qtdmri_fit.fitted_signal(),
qtdmri_fit_norm.fitted_signal())
rt_grid = qtdmri.create_rt_space_grid(5, 20e-3, 5, 0.02, .05)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
pdf = qtdmri_fit.pdf(rt_grid)
pdf_norm = qtdmri_fit_norm.pdf(rt_grid)
assert_array_almost_equal(pdf / pdf.max(), pdf_norm / pdf.max())
norm_laplacian = qtdmri_fit.norm_of_laplacian_signal()
norm_laplacian_norm = qtdmri_fit_norm.norm_of_laplacian_signal()
assert_array_almost_equal(norm_laplacian / norm_laplacian,
norm_laplacian_norm / norm_laplacian)