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_multi_tissue_mc_model():
scheme = wu_minn_hcp_acquisition_scheme()
ball = gaussian_models.G1Ball()
cyl = cylinder_models.C1Stick()
models = [ball, cyl]
S0_responses = [1., 2.]
mt_mc = modeling_framework.MultiCompartmentModel(
models=models, S0_tissue_responses=S0_responses)
mt_mc.set_fixed_parameter('C1Stick_1_lambda_par', 1.7e-9)
mt_mc.set_fixed_parameter('G1Ball_1_lambda_iso', 3e-9)
mt_mc.set_fixed_parameter('C1Stick_1_mu', [0., 0.])
param_dict = {'partial_volume_0': .5, 'partial_volume_1': .5}
E = mt_mc.simulate_signal(scheme, param_dict)
mt_mc_fit = mt_mc.fit(scheme, E)
sig_fracts = mt_mc_fit.fitted_parameters
vol_fracts = mt_mc_fit.fitted_multi_tissue_fractions
vol_fracts_norm = mt_mc_fit.fitted_multi_tissue_fractions_normalized
assert_almost_equal(
sig_fracts['partial_volume_0'], vol_fracts['partial_volume_0'], 2)
assert_almost_equal(
sig_fracts['partial_volume_1'], vol_fracts['partial_volume_1'] * 2., 2)
assert_almost_equal(
vol_fracts_norm['partial_volume_0'], 2 / 3., 2)
assert_almost_equal(
vol_fracts_norm['partial_volume_1'], 1 / 3., 2)
def test_acq_scheme_without_deltas_model_catch():
scheme = wu_minn_hcp_acquisition_scheme()
test_data = np.random.rand(len(scheme.bvalues))
scheme_clinical = acquisition_scheme_from_bvalues(
scheme.bvalues, scheme.gradient_directions)
mc_model = MultiCompartmentModel([C4CylinderGaussianPhaseApproximation()])
assert_raises(ValueError, mc_model.fit, scheme_clinical, test_data)
def test_all_models_dispersable():
scheme = wu_minn_hcp_acquisition_scheme()
dispersable_models = [
[cylinder_models.C1Stick()],
[cylinder_models.C2CylinderStejskalTannerApproximation()],
[cylinder_models.C3CylinderCallaghanApproximation()],
[cylinder_models.C4CylinderGaussianPhaseApproximation()],
[gaussian_models.G1Ball(),
gaussian_models.G2Zeppelin()], [gaussian_models.G3TemporalZeppelin()],
[sphere_models.S1Dot(),
gaussian_models.G2Zeppelin()],
[
sphere_models.S2SphereStejskalTannerApproximation(),
gaussian_models.G2Zeppelin()
]
]
spherical_distributions = [
distribute_models.SD1WatsonDistributed,
distribute_models.SD2BinghamDistributed
]
for model in dispersable_models:
for distribution in spherical_distributions:
dist_mod = distribution(model)
params = {}
for param, card in dist_mod.parameter_cardinality.items():
params[param] = np.random.rand(
card) * dist_mod.parameter_scales[param]
assert_equal(isinstance(dist_mod(scheme, **params), np.ndarray),
True)
def test_all_models_dispersable():
scheme = wu_minn_hcp_acquisition_scheme()
dispersable_models = [
cylinder_models.C1Stick,
cylinder_models.C2CylinderSodermanApproximation,
cylinder_models.C3CylinderCallaghanApproximation,
cylinder_models.C4CylinderGaussianPhaseApproximation,
gaussian_models.G2Zeppelin, gaussian_models.G3RestrictedZeppelin
]
spherical_distributions = [
distribute_models.SD1WatsonDistributed,
distribute_models.SD2BinghamDistributed
]
for model in dispersable_models:
for distribution in spherical_distributions:
mod = model()
dist_mod = distribution([mod])
params = {}
for param, card in dist_mod.parameter_cardinality.items():
params[param] = np.random.rand(
card) * dist_mod.parameter_scales[param]
assert_equal(isinstance(dist_mod(scheme, **params), np.ndarray),
True)
def test_acquisition_scheme_pruning():
scheme = wu_minn_hcp_acquisition_scheme()
test_data = np.random.rand(len(scheme.bvalues))
scheme_pr, data_pr = scheme.return_pruned_acquisition_scheme([2],
test_data)
assert_array_equal(scheme_pr.bvalues,
scheme.bvalues[scheme.shell_indices == 2])
assert_array_equal(data_pr, test_data[scheme.shell_indices == 2])
def test_raise_mix_with_tortuosity_in_mcmodel():
scheme = wu_minn_hcp_acquisition_scheme()
stick = cylinder_models.C1Stick()
zeppelin = gaussian_models.G2Zeppelin()
mc = modeling_framework.MultiCompartmentModel([stick, zeppelin])
mc.set_tortuous_parameter('G2Zeppelin_1_lambda_perp',
'C1Stick_1_lambda_par', 'partial_volume_0',
'partial_volume_1')
data = stick(scheme, lambda_par=1.7e-9, mu=[0., 0.])
assert_raises(ValueError, mc.fit, scheme, data, solver='mix')
def test_amico(lambda_1=[0, 0.0001], lambda_2=[0, 0.0000001], Nt=12):
"""Tests amico optimizer."""
"""
Arguments:
lambda_1: list
list of L1 regularization constants for each compartment
lambda_2: list
list of L2 regularization constants for each compartment
Nt: int
number of samples for tessellation of parameter range
"""
mc_model = create_noddi_watson_model()
scheme_hcp = saved_acquisition_schemes.wu_minn_hcp_acquisition_scheme()
grid_params = [
p for p in mc_model.parameter_names
if not p.endswith('mu') and not p.startswith('partial_volume')
]
x0_len = 0
for m_idx in range(len(mc_model.models)):
m_atoms = 1
for p in mc_model.models[m_idx].parameter_names:
if mc_model.model_names[m_idx] + p in grid_params:
m_atoms *= Nt
x0_len += m_atoms
x0_vector = np.zeros(x0_len)
amico_opt = amico_cvxpy.AmicoCvxpyOptimizer(mc_model,
scheme_hcp,
x0_vector=x0_vector,
lambda_1=lambda_1,
lambda_2=lambda_2)
signals, gt, dirs = simulate_signals(mc_model, scheme_hcp)
v_iso_estim = np.zeros(signals.shape[0])
v_ic_estim = np.zeros(signals.shape[0])
od_estim = np.zeros(signals.shape[0])
for i in range(signals.shape[0]):
model_dirs = [dirs[i, :]]
M, grid, idx = \
forward_model_matrix(mc_model, scheme_hcp, model_dirs, Nt)
parameters = amico_opt(signals[i, :], M, grid, idx)
v_iso_estim[i] = parameters[0]
od_estim[i] = parameters[2]
v_ic_estim[i] = parameters[3]
assert_almost_equal(v_iso_estim, gt['partial_volume_0'], 2)
assert_almost_equal(v_ic_estim,
gt['SD1WatsonDistributed_1_partial_volume_0'], 2)
assert_almost_equal(od_estim, gt['SD1WatsonDistributed_1_SD1Watson_1_odi'],
1)
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_all_models_distributable():
scheme = wu_minn_hcp_acquisition_scheme()
distributable_models = [
plane_models.P3PlaneCallaghanApproximation,
cylinder_models.C2CylinderStejskalTannerApproximation,
cylinder_models.C3CylinderCallaghanApproximation,
cylinder_models.C4CylinderGaussianPhaseApproximation,
sphere_models.S2SphereStejskalTannerApproximation
]
spatial_distributions = [distribute_models.DD1GammaDistributed]
for model in distributable_models:
for distribution in spatial_distributions:
mod = model()
dist_mod = distribution([mod])
params = {}
for param, card in dist_mod.parameter_cardinality.items():
params[param] = np.random.rand(
card) * dist_mod.parameter_scales[param]
assert_equal(isinstance(dist_mod(scheme, **params), np.ndarray),
True)
from dmipy.signal_models import cylinder_models, gaussian_models
from dmipy.core import modeling_framework
from dmipy.data.saved_acquisition_schemes import (
wu_minn_hcp_acquisition_scheme)
from numpy.testing import assert_almost_equal
scheme = wu_minn_hcp_acquisition_scheme()
def test_multi_tissue_mc_model():
scheme = wu_minn_hcp_acquisition_scheme()
ball = gaussian_models.G1Ball()
cyl = cylinder_models.C1Stick()
models = [ball, cyl]
S0_responses = [1., 2.]
mt_mc = modeling_framework.MultiCompartmentModel(
models=models, S0_tissue_responses=S0_responses)
mt_mc.set_fixed_parameter('C1Stick_1_lambda_par', 1.7e-9)
mt_mc.set_fixed_parameter('G1Ball_1_lambda_iso', 3e-9)
mt_mc.set_fixed_parameter('C1Stick_1_mu', [0., 0.])
param_dict = {'partial_volume_0': .5, 'partial_volume_1': .5}
E = mt_mc.simulate_signal(scheme, param_dict)
mt_mc_fit = mt_mc.fit(scheme, E)
sig_fracts = mt_mc_fit.fitted_parameters
vol_fracts = mt_mc_fit.fitted_multi_tissue_fractions
vol_fracts_norm = mt_mc_fit.fitted_multi_tissue_fractions_normalized
assert_almost_equal(
sig_fracts['partial_volume_0'], vol_fracts['partial_volume_0'], 2)
