def test_multivox_mapmri():
gtab = get_3shell_gtab()
data = np.random.random([20, 30, 1, gtab.gradients.shape[0]])
radial_order = 4
map_model = MapmriModel(gtab, radial_order=radial_order)
mapfit = map_model.fit(data)
c_map = mapfit.mapmri_coeff
assert_equal(c_map.shape[0:3], data.shape[0:3])
assert_equal(np.alltrue(np.isreal(c_map)), True)
def test_multivox_shore():
gtab = get_3shell_gtab()
data = np.random.random([20, 30, 1, gtab.gradients.shape[0]])
radial_order = 4
zeta = 700
asm = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=1e-8, lambdaL=1e-8)
asmfit = asm.fit(data)
c_shore=asmfit.shore_coeff
assert_equal(c_shore.shape[0:3], data.shape[0:3])
assert_equal(np.alltrue(np.isreal(c_shore)), True)
def setup():
data.gtab = get_3shell_gtab()
data.mevals = np.array(([0.0017, 0.0003, 0.0003],
[0.0017, 0.0003, 0.0003]))
data.angl = [(0, 0), (60, 0)]
data.S, data.sticks = MultiTensor(
data.gtab, data.mevals, S0=100.0, angles=data.angl,
fractions=[50, 50], snr=None)
data.sh_order = 6
data.lambda_lb = 1e-8
data.lambda_csd = 1.0
sphere = get_sphere('repulsion100')
data.sphere = sphere.vertices[0:int(sphere.vertices.shape[0]/2), :]
def get_test_data():
gtab = get_3shell_gtab()
evals_list = [
np.array([1.7E-3, 0.4E-3, 0.4E-3]),
np.array([6.0E-4, 4.0E-4, 4.0E-4]),
np.array([3.0E-3, 3.0E-3, 3.0E-3])
]
s0 = [0.8, 1, 4]
signals = [single_tensor(gtab, x[0], x[1]) for x in zip(s0, evals_list)]
tissues = [0, 0, 2, 0, 1, 0, 0, 1, 2] # wm=0, gm=1, csf=2
data = [add_noise(signals[tissue], 80, s0[0]) for tissue in tissues]
data = np.asarray(data).reshape((3, 3, 1, len(signals[0])))
evals = [evals_list[tissue] for tissue in tissues]
evals = np.asarray(evals).reshape((3, 3, 1, 3))
tissues = np.asarray(tissues).reshape((3, 3, 1))
masks = [np.where(tissues == x, 1, 0) for x in range(3)]
responses = [np.concatenate((x[0], [x[1]])) for x in zip(evals_list, s0)]
return (gtab, data, masks, responses)
def test_multivox_forecast():
gtab = get_3shell_gtab()
mevals = np.array(([0.0017, 0.0003, 0.0003], [0.0017, 0.0003, 0.0003]))
angl1 = [(0, 0), (60, 0)]
angl2 = [(90, 0), (45, 90)]
angl3 = [(0, 0), (90, 0)]
S = np.zeros((3, 1, 1, len(gtab.bvals)))
S[0, 0, 0], _ = multi_tensor(gtab,
mevals,
S0=1.0,
angles=angl1,
fractions=[50, 50],
snr=None)
S[1, 0, 0], _ = multi_tensor(gtab,
mevals,
S0=1.0,
angles=angl2,
fractions=[50, 50],
snr=None)
S[2, 0, 0], _ = multi_tensor(gtab,
mevals,
S0=1.0,
angles=angl3,
fractions=[50, 50],
snr=None)
fm = ForecastModel(gtab, sh_order=8, dec_alg='CSD')
f_fit = fm.fit(S)
S_predict = f_fit.predict()
assert_equal(S_predict.shape, S.shape)
mse1 = np.sum((S_predict[0, 0, 0] - S[0, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse1, 0.0, 3)
mse2 = np.sum((S_predict[1, 0, 0] - S[1, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse2, 0.0, 3)
mse3 = np.sum((S_predict[2, 0, 0] - S[2, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse3, 0.0, 3)
def test_MultiShellDeconvModel_response():
gtab = get_3shell_gtab()
sh_order = 8
response = multi_shell_fiber_response(sh_order, [0, 1000, 2000, 3500],
wm_response,
gm_response,
csf_response)
model_1 = MultiShellDeconvModel(gtab, response, sh_order=sh_order)
responses = np.array([wm_response, gm_response, csf_response])
model_2 = MultiShellDeconvModel(gtab, responses, sh_order=sh_order)
response_1 = model_1.response.response
response_2 = model_2.response.response
npt.assert_array_almost_equal(response_1, response_2, 0)
npt.assert_raises(ValueError, MultiShellDeconvModel,
gtab, np.ones((4, 3, 4)))
npt.assert_raises(ValueError, MultiShellDeconvModel,
gtab, np.ones((3, 3, 4)), iso=3)
def test_multivox_shore():
gtab = get_3shell_gtab()
data = np.random.random([20, 30, 1, gtab.gradients.shape[0]])
radial_order = 4
zeta = 700
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=1e-8,
lambdaL=1e-8)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
asmfit = asm.fit(data)
c_shore = asmfit.shore_coeff
npt.assert_equal(c_shore.shape[0:3], data.shape[0:3])
npt.assert_equal(np.alltrue(np.isreal(c_shore)), True)
def test_mapmri_odf():
gtab = get_3shell_gtab()
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
evals = np.array(([0.0017, 0.0003, 0.0003], [0.0017, 0.0003, 0.0003]))
data, golden_directions = MultiTensor(gtab,
evals,
S0=1.0,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
map_model = MapmriModel(gtab, radial_order=4)
# symmetric724
mapfit = map_model.fit(data)
odf = mapfit.odf(sphere)
directions, _, _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
# 5 subdivisions
odf = mapfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
mapfit = map_model.fit(data)
odf = mapfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
def test_mapmri_odf():
gtab = get_3shell_gtab()
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
evals = np.array(([0.0017, 0.0003, 0.0003],
[0.0017, 0.0003, 0.0003]))
data, golden_directions = MultiTensor(
gtab, evals, S0=1.0, angles=[(0, 0), (90, 0)], fractions=[50, 50],
snr=None)
map_model = MapmriModel(gtab, radial_order=4)
# symmetric724
mapfit = map_model.fit(data)
odf = mapfit.odf(sphere)
directions, _, _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, golden_directions), 2, 1)
# 5 subdivisions
odf = mapfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, golden_directions), 2, 1)
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
mapfit = map_model.fit(data)
odf = mapfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
def test_multi_shell_fiber_response():
gtab = get_3shell_gtab()
sh_order = 8
response = multi_shell_fiber_response(sh_order, [0, 1000, 2000, 3500],
wm_response, gm_response,
csf_response)
npt.assert_equal(response.response.shape, (4, 7))
with warnings.catch_warnings(record=True) as w:
response = multi_shell_fiber_response(sh_order, [1000, 2000, 3500],
wm_response, gm_response,
csf_response)
# Test that the number of warnings raised is greater than 1, with
# deprecation warnings being raised from using legacy SH bases as well
# as a warning from multi_shell_fiber_response
npt.assert_(len(w) > 1)
# The last warning in list is the one from multi_shell_fiber_response
npt.assert_(issubclass(w[-1].category, UserWarning))
npt.assert_(
"""No b0 given. Proceeding either way.""" in str(w[-1].message))
npt.assert_equal(response.response.shape, (3, 7))
def test_MSDeconvFit():
gtab = get_3shell_gtab()
mevals = np.array([wm_response[0, :3], wm_response[0, :3]])
angles = [(0, 0), (60, 0)]
S_wm, sticks = multi_tensor(gtab, mevals, wm_response[0, 3], angles=angles,
fractions=[30., 70.], snr=None)
S_gm = gm_response[0, 3] * np.exp(-gtab.bvals * gm_response[0, 0])
S_csf = csf_response[0, 3] * np.exp(-gtab.bvals * csf_response[0, 0])
sh_order = 8
response = multi_shell_fiber_response(sh_order, [0, 1000, 2000, 3500],
wm_response,
gm_response,
csf_response)
model = MultiShellDeconvModel(gtab, response)
vf = [0.325, 0.2, 0.475]
signal = sum(i * j for i, j in zip(vf, [S_csf, S_gm, S_wm]))
fit = model.fit(signal)
# Testing volume fractions
npt.assert_array_almost_equal(fit.volume_fractions, vf, 1)
def test_multivox_forecast():
gtab = get_3shell_gtab()
mevals = np.array(([0.0017, 0.0003, 0.0003],
[0.0017, 0.0003, 0.0003]))
angl1 = [(0, 0), (60, 0)]
angl2 = [(90, 0), (45, 90)]
angl3 = [(0, 0), (90, 0)]
S = np.zeros((3, 1, 1, len(gtab.bvals)))
S[0, 0, 0], sticks = MultiTensor(
gtab, mevals, S0=1.0, angles=angl1,
fractions=[50, 50], snr=None)
S[1, 0, 0], sticks = MultiTensor(
gtab, mevals, S0=1.0, angles=angl2,
fractions=[50, 50], snr=None)
S[2, 0, 0], sticks = MultiTensor(
gtab, mevals, S0=1.0, angles=angl3,
fractions=[50, 50], snr=None)
fm = ForecastModel(gtab, sh_order=8,
dec_alg='CSD')
f_fit = fm.fit(S)
S_predict = f_fit.predict()
assert_equal(S_predict.shape, S.shape)
mse1 = np.sum((S_predict[0, 0, 0]-S[0, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse1, 0.0, 3)
mse2 = np.sum((S_predict[1, 0, 0]-S[1, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse2, 0.0, 3)
mse3 = np.sum((S_predict[2, 0, 0]-S[2, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse3, 0.0, 3)
def test_compartments():
# test for failure if no. of compartments less than 2
gtab = get_3shell_gtab()
sh_order = 8
response = sim_response(sh_order, [0, 1000, 2000, 3500])
npt.assert_raises(ValueError, MultiShellDeconvModel, gtab, response, iso=1)
