def test_spherical_harmonics_model_raises(odi=0.15,
mu=[0., 0.],
lambda_par=1.7e-9):
stick = cylinder_models.C1Stick()
ball = gaussian_models.G1Ball()
watsonstick = distribute_models.SD1WatsonDistributed([stick])
params = {
'SD1Watson_1_odi': odi,
'SD1Watson_1_mu': mu,
'C1Stick_1_lambda_par': lambda_par
}
data = watsonstick(scheme, **params)
assert_raises(ValueError,
modeling_framework.MultiCompartmentSphericalHarmonicsModel,
[ball])
sh_mod = modeling_framework.MultiCompartmentSphericalHarmonicsModel(
[stick])
assert_raises(ValueError, sh_mod.fit, scheme, data, solver='csd_cvxpy')
sh_mod = modeling_framework.MultiCompartmentSphericalHarmonicsModel(
[stick, ball])
sh_mod.set_fixed_parameter('C1Stick_1_lambda_par', lambda_par)
sh_mod.set_fixed_parameter('G1Ball_1_lambda_iso', 3e-9)
assert_raises(ValueError,
sh_mod.fit,
scheme,
data,
solver='csd_tournier07')
def test_multi_compartment_fod_with_parametric_model(
odi=0.15, mu=[0., 0.], lambda_iso=3e-9, lambda_par=1.7e-9,
vf_intra=0.7):
stick = cylinder_models.C1Stick()
ball = gaussian_models.G1Ball()
watsonstick = distribute_models.SD1WatsonDistributed(
[stick])
mc_mod = modeling_framework.MultiCompartmentModel([watsonstick, ball])
sh_mod = modeling_framework.MultiCompartmentSphericalHarmonicsModel(
[stick, ball])
sh_mod.set_fixed_parameter('G1Ball_1_lambda_iso', lambda_iso)
sh_mod.set_fixed_parameter('C1Stick_1_lambda_par', lambda_par)
simulation_parameters = mc_mod.parameters_to_parameter_vector(
G1Ball_1_lambda_iso=lambda_iso,
SD1WatsonDistributed_1_SD1Watson_1_mu=mu,
SD1WatsonDistributed_1_C1Stick_1_lambda_par=lambda_par,
SD1WatsonDistributed_1_SD1Watson_1_odi=odi,
partial_volume_0=vf_intra,
partial_volume_1=1 - vf_intra)
data = mc_mod.simulate_signal(scheme, simulation_parameters)
sh_fit = sh_mod.fit(scheme, data)
vf_intra_estimated = sh_fit.fitted_parameters['partial_volume_0']
assert_almost_equal(vf_intra, vf_intra_estimated)
predicted_signal = sh_fit.predict()
assert_array_almost_equal(data, predicted_signal[0], 4)
def test_equivalence_csd_and_parametric_fod(
odi=0.15, mu=[0., 0.], lambda_par=1.7e-9):
stick = cylinder_models.C1Stick()
watsonstick = distribute_models.SD1WatsonDistributed(
[stick])
params = {'SD1Watson_1_odi': odi,
'SD1Watson_1_mu': mu,
'C1Stick_1_lambda_par': lambda_par}
data = watsonstick(scheme, **params)
sh_mod = modeling_framework.MultiCompartmentSphericalHarmonicsModel(
[stick])
sh_mod.set_fixed_parameter('C1Stick_1_lambda_par', lambda_par)
sh_fit = sh_mod.fit(scheme, data)
fod = sh_fit.fod(sphere.vertices)
watson = distributions.SD1Watson(mu=[0., 0.], odi=0.15)
sf = watson(sphere.vertices)
assert_array_almost_equal(fod[0], sf, 2)
fitted_signal = sh_fit.predict()
assert_array_almost_equal(data, fitted_signal[0], 4)
def test_laplacian_and_AI_with_regularization(odi=0.15,
mu=[0., 0.],
lambda_par=1.7e-9):
stick = cylinder_models.C1Stick()
watsonstick = distribute_models.SD1WatsonDistributed([stick])
params = {
'SD1Watson_1_odi': odi,
'SD1Watson_1_mu': mu,
'C1Stick_1_lambda_par': lambda_par
}
data = watsonstick(scheme, **params)
sh_mod = modeling_framework.MultiCompartmentSphericalHarmonicsModel(
[stick])
sh_mod.set_fixed_parameter('C1Stick_1_lambda_par', lambda_par)
for solver in ['csd_tournier07', 'csd_cvxpy']:
sh_fit = sh_mod.fit(scheme, data, solver=solver, lambda_lb=0.)
sh_fit_reg = sh_mod.fit(scheme, data, solver=solver, lambda_lb=1e-3)
ai = sh_fit.anisotropy_index()
lb = sh_fit.norm_of_laplacian_fod()
ai_reg = sh_fit_reg.anisotropy_index()
lb_reg = sh_fit_reg.norm_of_laplacian_fod()
assert_(ai > ai_reg)
assert_(lb > lb_reg)
def test_equivalence_sh_distributed_mc_with_mcsh():
"""
We test if we can input a Watson-distributed zeppelin and stick into an
SD3SphericalHarmonicsDistributedModel in an MC-model, and compare it with
an MCSH model with the same watson distribution as a kernel.
"""
stick = cylinder_models.C1Stick()
zep = gaussian_models.G2Zeppelin()
mck_dist = distribute_models.SD1WatsonDistributed([stick, zep])
mck_dist.set_equal_parameter('G2Zeppelin_1_lambda_par',
'C1Stick_1_lambda_par')
mck_dist.set_tortuous_parameter('G2Zeppelin_1_lambda_perp',
'G2Zeppelin_1_lambda_par',
'partial_volume_0')
mcsh = modeling_framework.MultiCompartmentSphericalHarmonicsModel(
models=[mck_dist], sh_order=8)
mc = modeling_framework.MultiCompartmentModel([
distribute_models.SD3SphericalHarmonicsDistributed([mck_dist],
sh_order=8)
])
lambda_par = 0.
odi = .02
sh_coeff = np.ones(45)
sh_coeff[0] = 1 / (2 * np.sqrt(np.pi))
pv0 = .3
params_mcsh = {
'SD1WatsonDistributed_1_partial_volume_0': pv0,
'SD1WatsonDistributed_1_G2Zeppelin_1_lambda_par': lambda_par,
'SD1WatsonDistributed_1_SD1Watson_1_odi': odi,
'sh_coeff': sh_coeff
}
basemod = 'SD3SphericalHarmonicsDistributed_1_'
params_mc = {
basemod + 'SD1WatsonDistributed_1_partial_volume_0': pv0,
basemod + 'SD1WatsonDistributed_1_G2Zeppelin_1_lambda_par': lambda_par,
basemod + 'SD1WatsonDistributed_1_SD1Watson_1_odi': odi,
basemod + 'SD3SphericalHarmonics_1_sh_coeff': sh_coeff
}
E_mcsh = mcsh.simulate_signal(scheme, params_mcsh)
E_mc = mc.simulate_signal(scheme, params_mc)
np.testing.assert_array_almost_equal(E_mcsh, E_mc)
