def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
assert_equal(len(w) > 0, False)
csd_fit = csd.fit(np.zeros_like(S))
fodf = csd_fit.odf(sphere)
assert_array_equal(fodf, np.zeros_like(fodf))
odf_sh = np.zeros_like(fodf)
odf_sh[1] = np.nan
fodf, it = odf_deconv(odf_sh, csd.R, csd.B_reg)
assert_array_equal(fodf, np.zeros_like(fodf))
def odf(self, sphere):
self.gqi_vector = self.model.cache_get('gqi_vector', key=sphere)
if self.gqi_vector is None:
if self.model.method == 'gqi2':
H = squared_radial_component
self.gqi_vector = np.real(H(np.dot(self.model.b_vector,
sphere.vertices.T) * self.model.Lambda / np.pi))
if self.model.method == 'standard':
self.gqi_vector = np.real(
np.sinc(np.dot(self.model.b_vector,
sphere.vertices.T) * self.model.Lambda / np.pi))
self.model.cache_set('gqi_vector', sphere, self.gqi_vector)
gqi_odf = np.dot(self.data, self.gqi_vector)
gqi_odf_sh = np.dot(gqi_odf, self.model.invB)
fodf_sh, num_it = odf_deconv(gqi_odf_sh,
self.model.sh_order,
self.model.R,
self.model.B_reg,
lambda_=self.model.lambda_,
tau=self.model.tau, r2_term=r2)
return np.dot(fodf_sh, self.model.invB.T)
def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
assert_equal(len(w) > 0, False)
csd_fit = csd.fit(np.zeros_like(S))
fodf = csd_fit.odf(sphere)
assert_array_equal(fodf, np.zeros_like(fodf))
odf_sh = np.zeros_like(fodf)
odf_sh[1] = np.nan
fodf, it = odf_deconv(odf_sh, csd.R, csd.B_reg)
assert_array_equal(fodf, np.zeros_like(fodf))
def odf(self, sphere):
r""" Calculates the real analytical odf for a given discrete sphere.
"""
upsilon = self.model.cache_get('shore_matrix_odf', key=sphere)
if upsilon is None:
upsilon = SHOREmatrix_odf(self.radial_order, self.zeta,
sphere.vertices)
self.model.cache_set('shore_matrix_odf', sphere, upsilon)
odf = np.dot(upsilon, self._shore_coef)
odf_sh = np.dot(odf, self.model.invB)
fodf_sh, num_it = odf_deconv(odf_sh,
self.model.sh_order,
self.model.R,
self.model.B_reg,
lambda_=self.model.lambda_,
tau=self.model.tau, r2_term=True)
return np.dot(fodf_sh, self.model.invB.T)
def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
S, _ = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
w_count = len(w)
# A warning is expected from the ConstrainedSDTModel constructor
# and additionnal warnings should be raised where legacy SH bases
# are used
assert_equal(w_count > 1, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
# Test that the warning from ConstrainedSDTModel
# constructor is no more raised
assert_equal(len(w) == w_count - 1, True)
csd_fit = csd.fit(np.zeros_like(S))
fodf = csd_fit.odf(sphere)
assert_array_equal(fodf, np.zeros_like(fodf))
odf_sh = np.zeros_like(fodf)
odf_sh[1] = np.nan
fodf, _ = odf_deconv(odf_sh, csd.R, csd.B_reg)
assert_array_equal(fodf, np.zeros_like(fodf))
