def test_C2_watson_gamma_equals_gamma_watson():
scheme = wu_minn_hcp_acquisition_scheme()
cylinder = cylinder_models.C2CylinderStejskalTannerApproximation()
watsoncyl = distribute_models.SD1WatsonDistributed([cylinder])
gammawatsoncyl = distribute_models.DD1GammaDistributed(
[watsoncyl],
target_parameter='C2CylinderStejskalTannerApproximation_1_diameter')
param_name = 'C2CylinderStejskalTannerApproximation_1_lambda_par'
params1 = {
'SD1WatsonDistributed_1_' + param_name: 1.7e-9,
'DD1Gamma_1_alpha': 2.,
'DD1Gamma_1_beta': 4e-6,
'SD1WatsonDistributed_1_SD1Watson_1_odi': 0.4,
'SD1WatsonDistributed_1_SD1Watson_1_mu': [0., 0.]
}
gammacyl = distribute_models.DD1GammaDistributed([cylinder])
watsongammacyl = distribute_models.SD1WatsonDistributed([gammacyl])
params2 = {
'DD1GammaDistributed_1_' + param_name: 1.7e-9,
'DD1GammaDistributed_1_DD1Gamma_1_alpha': 2.,
'DD1GammaDistributed_1_DD1Gamma_1_beta': 4e-6,
'SD1Watson_1_odi': 0.4,
'SD1Watson_1_mu': [0., 0.]
}
assert_array_almost_equal(watsongammacyl(scheme, **params2),
gammawatsoncyl(scheme, **params1), 5)
def test_all_fitted_model_properties():
stick = cylinder_models.C1Stick()
watsonstick = distribute_models.SD1WatsonDistributed([stick])
params = {}
for parameter, card, in watsonstick.parameter_cardinality.items():
params[parameter] = (np.random.rand(card) *
watsonstick.parameter_scales[parameter])
data = np.atleast_2d(watsonstick(scheme, **params))
mcmod = modeling_framework.MultiCompartmentModel([watsonstick])
mcfit = mcmod.fit(scheme, data)
vertices = np.random.rand(10, 3)
vertices /= np.linalg.norm(vertices, axis=1)[:, None]
assert_(isinstance(mcfit.fitted_parameters, dict))
assert_(isinstance(mcfit.fitted_parameters_vector, np.ndarray))
assert_(isinstance(mcfit.fod(vertices), np.ndarray))
assert_(isinstance(mcfit.fod_sh(), np.ndarray))
assert_(isinstance(mcfit.peaks_spherical(), np.ndarray))
assert_(isinstance(mcfit.peaks_cartesian(), np.ndarray))
assert_(isinstance(mcfit.mean_squared_error(data), np.ndarray))
assert_(isinstance(mcfit.R2_coefficient_of_determination(data),
np.ndarray))
assert_(isinstance(mcfit.predict(), np.ndarray))
mcmod_sm = modeling_framework.MultiCompartmentSphericalMeanModel([stick])
mcfit_sm = mcmod_sm.fit(scheme, data)
assert_(isinstance(mcfit_sm.fitted_parameters, dict))
assert_(isinstance(mcfit_sm.fitted_parameters_vector, np.ndarray))
assert_(isinstance(mcfit.mean_squared_error(data), np.ndarray))
assert_(isinstance(mcfit.R2_coefficient_of_determination(data),
np.ndarray))
assert_(isinstance(mcfit.predict(), np.ndarray))
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_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_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_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_set_fixed_parameter_raises():
cyl = cylinder_models.C1Stick()
distcyl = distribute_models.SD1WatsonDistributed([cyl])
assert_raises(ValueError, distcyl.set_fixed_parameter, 'SD1Watson_1_odi',
[1])
assert_raises(ValueError, distcyl.set_fixed_parameter, 'SD1Watson_1_mu',
[1])
assert_raises(ValueError, distcyl.set_fixed_parameter, 'blabla', [1])
def test_set_equal_param():
cylinder = cylinder_models.C2CylinderStejskalTannerApproximation()
watsoncyl = distribute_models.SD1WatsonDistributed([cylinder])
p1 = 'C2CylinderStejskalTannerApproximation_1_lambda_par'
p2 = 'C2CylinderStejskalTannerApproximation_1_diameter'
watsoncyl.set_equal_parameter(p1, p2)
isnone = False
reset = watsoncyl.set_equal_parameter(p1, p2)
if reset is None:
isnone = True
assert_equal(isnone, True)
def test_C4_watson_gamma_equals_gamma_watson():
scheme = wu_minn_hcp_acquisition_scheme()
cylinder = cylinder_models.C4CylinderGaussianPhaseApproximation()
watsoncyl = distribute_models.SD1WatsonDistributed([cylinder])
gammawatsoncyl = distribute_models.DD1GammaDistributed(
[watsoncyl],
target_parameter='C4CylinderGaussianPhaseApproximation_1_diameter')
param = 'SD1WatsonDistributed_1_C4CylinderGaussianPhaseApproximation'
param += '_1_lambda_par'
params1 = {
param: 1.7e-9,
'DD1Gamma_1_alpha': 2.,
'DD1Gamma_1_beta': 4e-6,
'SD1WatsonDistributed_1_SD1Watson_1_odi': 0.4,
'SD1WatsonDistributed_1_SD1Watson_1_mu': [0., 0.]
}
gammacyl = distribute_models.DD1GammaDistributed([cylinder])
watsongammacyl = distribute_models.SD1WatsonDistributed(
[gammacyl],
target_parameter='C4CylinderGaussianPhaseApproximation_1_mu')
param = 'DD1GammaDistributed_1_C4CylinderGaussianPhaseApproximation'
param += '_1_lambda_par'
params2 = {
param: 1.7e-9,
'DD1GammaDistributed_1_DD1Gamma_1_alpha': 2.,
'DD1GammaDistributed_1_DD1Gamma_1_beta': 4e-6,
'SD1Watson_1_odi': 0.4,
'SD1Watson_1_mu': [0., 0.]
}
assert_array_almost_equal(watsongammacyl(scheme, **params2),
gammawatsoncyl(scheme, **params1), 5)
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)
def test_construction_observation_matrix(
odi=0.15, mu=[0., 0.], lambda_par=1.7e-9, lmax=8):
stick = cylinder_models.C1Stick(lambda_par=lambda_par)
watsonstick = distribute_models.SD1WatsonDistributed(
[stick])
params = {'SD1Watson_1_odi': odi,
'SD1Watson_1_mu': mu,
'C1Stick_1_lambda_par': lambda_par}
data = watsonstick(scheme, **params)
watson = distributions.SD1Watson(mu=mu, odi=odi)
sh_watson = watson.spherical_harmonics_representation(lmax)
stick_rh = stick.rotational_harmonics_representation(scheme)
A = construct_model_based_A_matrix(scheme, stick_rh, lmax)
data_approximated = A.dot(sh_watson)
np.testing.assert_array_almost_equal(data_approximated, data, 4)
def _make_single_and_crossing():
zeppelin = G2Zeppelin(lambda_par=1.7e-9, lambda_perp=1e-9, mu=[0., 0.])
data_aniso = zeppelin(scheme)
S0_aniso, aniso_model = estimate_TR2_anisotropic_tissue_response_model(
scheme, np.atleast_2d(data_aniso))
watson_mod = distribute_models.SD1WatsonDistributed([aniso_model])
watson_params_par = {
'SD1Watson_1_mu': np.array([0., 0.]),
'SD1Watson_1_odi': .2
}
watson_params_perp = {
'SD1Watson_1_mu': np.array([np.pi / 2., np.pi / 2.]),
'SD1Watson_1_odi': .2
}
data_watson_par = watson_mod(scheme, **watson_params_par)
data_watson_perp = watson_mod(scheme, **watson_params_perp)
data_cross = np.array(
[data_watson_par, data_watson_par + data_watson_perp])
return data_cross
def test_watson_dispersed_stick_tortuous_zeppelin():
stick = cylinder_models.C1Stick()
zeppelin = gaussian_models.G2Zeppelin()
watson_bundle = distribute_models.SD1WatsonDistributed(
models=[stick, zeppelin])
watson_bundle.set_tortuous_parameter(
'G2Zeppelin_1_lambda_perp',
'G2Zeppelin_1_lambda_par',
'partial_volume_0'
)
watson_bundle.set_equal_parameter(
'G2Zeppelin_1_lambda_par',
'C1Stick_1_lambda_par')
watson_bundle.set_fixed_parameter(
'G2Zeppelin_1_lambda_par', 1.7e-9)
mc_watson = (
modeling_framework.MultiCompartmentModel(
models=[watson_bundle])
)
beta0 = camino_dispersed.beta == 0.
diff17 = camino_dispersed.diffusivities == 1.7e-9
mask = np.all([beta0, diff17], axis=0)
E_watson = camino_dispersed.signal_attenuation[mask]
fractions_watson = camino_dispersed.fractions[mask]
fitted_params = (
mc_watson.fit(scheme, E_watson[::20]).fitted_parameters
)
mean_abs_error = np.mean(
abs(fitted_params['SD1WatsonDistributed_1_partial_volume_0'].squeeze(
) - fractions_watson[::20]))
assert_equal(mean_abs_error < 0.03, True)
def test_parametric_fod_spherical_mean_model():
stick = cylinder_models.C1Stick()
watsonstick = distribute_models.SD1WatsonDistributed([stick])
params = {}
for parameter, card, in watsonstick.parameter_cardinality.items():
params[parameter] = (np.random.rand(card) *
watsonstick.parameter_scales[parameter])
data = np.atleast_2d(watsonstick(scheme, **params))
stick = cylinder_models.C1Stick()
zeppelin = gaussian_models.G2Zeppelin()
smt = modeling_framework.MultiCompartmentSphericalMeanModel(
[stick, zeppelin])
smt.set_tortuous_parameter('G2Zeppelin_1_lambda_perp',
'C1Stick_1_lambda_par', 'partial_volume_0',
'partial_volume_1')
smt.set_equal_parameter('G2Zeppelin_1_lambda_par', 'C1Stick_1_lambda_par')
smt_fit = smt.fit(scheme, data)
assert_raises(ValueError,
smt_fit.return_parametric_fod_model,
Ncompartments=1.5)
assert_raises(ValueError,
smt_fit.return_parametric_fod_model,
Ncompartments=0)
assert_raises(ValueError,
smt_fit.return_parametric_fod_model,
distribution='bla')
for distribution_name in ['watson', 'bingham']:
fod_model = smt_fit.return_parametric_fod_model(
distribution=distribution_name, Ncompartments=1)
fitted_fod_model = fod_model.fit(scheme, data)
assert_(isinstance(fitted_fod_model.fitted_parameters, dict))
def test_tissue_response_model_multi_compartment_models():
ball = G1Ball(lambda_iso=2.5e-9)
data_iso = ball(scheme)
data_iso_sm = ball.spherical_mean(scheme)
S0_iso, iso_model = estimate_TR1_isotropic_tissue_response_model(
scheme, np.atleast_2d(data_iso))
zeppelin = G2Zeppelin(lambda_par=1.7e-9,
lambda_perp=1e-9,
mu=[np.pi / 2, np.pi / 2])
data_aniso = zeppelin(scheme)
data_aniso_sm = zeppelin.spherical_mean(scheme)
S0_aniso, aniso_model = estimate_TR2_anisotropic_tissue_response_model(
scheme, np.atleast_2d(data_aniso))
models = [iso_model, aniso_model]
mc = MultiCompartmentModel(models)
mc_smt = MultiCompartmentSphericalMeanModel(models)
test_mc_data = 0.5 * data_iso + 0.5 * data_aniso
test_mc_data_sm = 0.5 * data_iso_sm + 0.5 * data_aniso_sm
test_data = [test_mc_data, test_mc_data_sm]
params = {
'partial_volume_0': [0.5],
'partial_volume_1': [0.5],
'TR2AnisotropicTissueResponseModel_1_mu':
np.array([np.pi / 2, np.pi / 2])
}
mc_models = [mc, mc_smt]
for model, data in zip(mc_models, test_data):
data_mc = model(scheme, **params)
assert_array_almost_equal(data, data_mc, 3)
# csd model with single models
mc_csd = MultiCompartmentSphericalHarmonicsModel([aniso_model])
watson_mod = distribute_models.SD1WatsonDistributed([aniso_model])
watson_params = {
'SD1Watson_1_mu': np.array([np.pi / 2, np.pi / 2]),
'SD1Watson_1_odi': .3
}
data_watson = watson_mod(scheme, **watson_params)
mc_csd_fit = mc_csd.fit(scheme, data_watson)
assert_array_almost_equal(mc_csd_fit.predict()[0], data_watson, 2)
# csd model with multiple models
mc_csd = MultiCompartmentSphericalHarmonicsModel(models)
watson_mod = distribute_models.SD1WatsonDistributed(
[iso_model, aniso_model])
watson_params = {
'SD1Watson_1_mu': np.array([np.pi / 2, np.pi / 2]),
'SD1Watson_1_odi': .3,
'partial_volume_0': 0.5
}
data_watson = watson_mod(scheme, **watson_params)
mc_csd_fit = mc_csd.fit(scheme, data_watson)
assert_array_almost_equal(mc_csd_fit.predict()[0], data_watson, 2)
scheme_panagiotaki = panagiotaki_verdict_acquisition_scheme()
assert_raises(ValueError,
mc_csd.fit,
acquisition_scheme=scheme_panagiotaki,
data=data_watson)
def test_raise_spherical_distribution_in_spherical_mean():
zeppelin = gaussian_models.G2Zeppelin()
watson = distribute_models.SD1WatsonDistributed([zeppelin])
assert_raises(ValueError,
modeling_framework.MultiCompartmentSphericalMeanModel,
[watson])
def test_multi_voxel_parametric_to_sm_to_sh_fod_watson():
stick = cylinder_models.C1Stick()
zeppelin = gaussian_models.G2Zeppelin()
watsonstick = distribute_models.SD1WatsonDistributed([stick, zeppelin])
watsonstick.set_equal_parameter('G2Zeppelin_1_lambda_par',
'C1Stick_1_lambda_par')
watsonstick.set_tortuous_parameter('G2Zeppelin_1_lambda_perp',
'G2Zeppelin_1_lambda_par',
'partial_volume_0')
mc_mod = modeling_framework.MultiCompartmentModel([watsonstick])
parameter_dict = {
'SD1WatsonDistributed_1_SD1Watson_1_mu':
np.random.rand(10, 2),
'SD1WatsonDistributed_1_partial_volume_0':
np.linspace(0.1, 0.9, 10),
'SD1WatsonDistributed_1_G2Zeppelin_1_lambda_par':
np.linspace(1.5, 2.5, 10) * 1e-9,
'SD1WatsonDistributed_1_SD1Watson_1_odi':
np.linspace(0.3, 0.7, 10)
}
data = mc_mod.simulate_signal(scheme, parameter_dict)
sm_mod = modeling_framework.MultiCompartmentSphericalMeanModel(
[stick, zeppelin])
sm_mod.set_equal_parameter('G2Zeppelin_1_lambda_par',
'C1Stick_1_lambda_par')
sm_mod.set_tortuous_parameter('G2Zeppelin_1_lambda_perp',
'G2Zeppelin_1_lambda_par',
'partial_volume_0', 'partial_volume_1')
sf_watson = []
for mu, odi in zip(
parameter_dict['SD1WatsonDistributed_1_SD1Watson_1_mu'],
parameter_dict['SD1WatsonDistributed_1_SD1Watson_1_odi']):
watson = distributions.SD1Watson(mu=mu, odi=odi)
sf_watson.append(watson(sphere.vertices))
sf_watson = np.array(sf_watson)
sm_fit = sm_mod.fit(scheme, data)
sh_mod = sm_fit.return_spherical_harmonics_fod_model()
sh_fit_auto = sh_mod.fit(scheme, data) # will pick tournier
fod_tournier = sh_fit_auto.fod(sphere.vertices)
assert_array_almost_equal(fod_tournier, sf_watson, 1)
sh_fit_tournier = sh_mod.fit(scheme,
data,
solver='csd_tournier07',
unity_constraint=False)
fod_tournier = sh_fit_tournier.fod(sphere.vertices)
assert_array_almost_equal(fod_tournier, sf_watson, 1)
sh_fit_cvxpy = sh_mod.fit(scheme,
data,
solver='csd_cvxpy',
unity_constraint=True,
lambda_lb=0.)
fod_cvxpy = sh_fit_cvxpy.fod(sphere.vertices)
assert_array_almost_equal(fod_cvxpy, sf_watson, 2)
sh_fit_cvxpy = sh_mod.fit(scheme,
data,
solver='csd_cvxpy',
unity_constraint=False,
lambda_lb=0.)
fod_cvxpy = sh_fit_cvxpy.fod(sphere.vertices)
assert_array_almost_equal(fod_cvxpy, sf_watson, 2)
