def test_mapmri_isotropic_design_matrix_separability(radial_order=6):
gtab = get_gtab_taiwan_dsi()
tau = 1 / (4 * np.pi ** 2)
qvals = np.sqrt(gtab.bvals / tau) / (2 * np.pi)
q = gtab.bvecs * qvals[:, None]
mu = 0.0003 # random value
M = mapmri.mapmri_isotropic_phi_matrix(radial_order, mu, q)
M_independent = mapmri.mapmri_isotropic_M_mu_independent(radial_order, q)
M_dependent = mapmri.mapmri_isotropic_M_mu_dependent(radial_order, mu,
qvals)
M_reconstructed = M_independent * M_dependent
assert_array_almost_equal(M, M_reconstructed)
def test_mapmri_isotropic_design_matrix_separability(radial_order=6):
gtab = get_gtab_taiwan_dsi()
tau = 1 / (4 * np.pi**2)
qvals = np.sqrt(gtab.bvals / tau) / (2 * np.pi)
q = gtab.bvecs * qvals[:, None]
mu = 0.0003 # random value
M = mapmri.mapmri_isotropic_phi_matrix(radial_order, mu, q)
M_independent = mapmri.mapmri_isotropic_M_mu_independent(radial_order, q)
M_dependent = mapmri.mapmri_isotropic_M_mu_dependent(
radial_order, mu, qvals)
M_reconstructed = M_independent * M_dependent
assert_array_almost_equal(M, M_reconstructed)
def test_mapmri_isotropic_design_matrix_separability(radial_order=6):
gtab = get_gtab_taiwan_dsi()
tau = 1 / (4 * np.pi**2)
qvals = np.sqrt(gtab.bvals / tau) / (2 * np.pi)
q = gtab.bvecs * qvals[:, None]
mu = 0.0003 # random value
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
M = mapmri.mapmri_isotropic_phi_matrix(radial_order, mu, q)
M_independent = mapmri.mapmri_isotropic_M_mu_independent(
radial_order, q)
M_dependent = mapmri.mapmri_isotropic_M_mu_dependent(
radial_order, mu, qvals)
M_reconstructed = M_independent * M_dependent
assert_array_almost_equal(M, M_reconstructed)