def test_bdg_residual():
"""This tests the bootstrapping residual.
"""
hsph_updated = HemiSphere.from_sphere(unit_icosahedron).subdivide(2)
vertices = hsph_updated.vertices
bvecs = vertices
bvals = np.ones(len(vertices)) * 1000
bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)
bvals = np.insert(bvals, 0, 0)
gtab = gradient_table(bvals, bvecs)
r, theta, phi = cart2sphere(*vertices.T)
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message=shm.descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
B, m, n = shm.real_sh_descoteaux(6, theta, phi)
shm_coeff = np.random.random(B.shape[1])
# sphere_func is sampled of the spherical function for each point of
# the sphere
sphere_func = np.dot(shm_coeff, B.T)
voxel = np.concatenate((np.zeros(1), sphere_func))
data = np.tile(voxel, (3, 3, 3, 1))
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message=shm.descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
boot_pmf_gen = BootPmfGen(data, model=csd_model, sphere=hsph_updated,
sh_order=6)
# Two boot samples should be the same
odf1 = boot_pmf_gen.get_pmf(np.array([1.5, 1.5, 1.5]))
odf2 = boot_pmf_gen.get_pmf(np.array([1.5, 1.5, 1.5]))
npt.assert_array_almost_equal(odf1, odf2)
# A boot sample with less sh coeffs should have residuals, thus the two
# should be different
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message=shm.descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
boot_pmf_gen2 = BootPmfGen(data, model=csd_model, sphere=hsph_updated,
sh_order=4)
odf1 = boot_pmf_gen2.get_pmf(np.array([1.5, 1.5, 1.5]))
odf2 = boot_pmf_gen2.get_pmf(np.array([1.5, 1.5, 1.5]))
npt.assert_(np.any(odf1 != odf2))
# test with a gtab with two shells and assert you get an error
bvals[-1] = 2000
gtab = gradient_table(bvals, bvecs)
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message=shm.descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
npt.assert_raises(ValueError, BootPmfGen, data, csd_model, hsph_updated, 6)
def test_bdg_residual():
"""This tests the bootstrapping residual.
"""
hsph_updated = HemiSphere.from_sphere(unit_icosahedron).subdivide(2)
vertices = hsph_updated.vertices
bvecs = vertices
bvals = np.ones(len(vertices)) * 1000
bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)
bvals = np.insert(bvals, 0, 0)
gtab = gradient_table(bvals, bvecs)
r, theta, phi = cart2sphere(*vertices.T)
B, m, n = shm.real_sym_sh_basis(6, theta, phi)
shm_coeff = np.random.random(B.shape[1])
# sphere_func is sampled of the spherical function for each point of
# the sphere
sphere_func = np.dot(shm_coeff, B.T)
voxel = np.concatenate((np.zeros(1), sphere_func))
data = np.tile(voxel, (3, 3, 3, 1))
csd_model = ConstrainedSphericalDeconvModel(gtab, None, sh_order=6)
boot_pmf_gen = BootPmfGen(data, model=csd_model, sphere=hsph_updated,
sh_order=6)
# Two boot samples should be the same
odf1 = boot_pmf_gen.get_pmf(np.array([1.5, 1.5, 1.5]))
odf2 = boot_pmf_gen.get_pmf(np.array([1.5, 1.5, 1.5]))
npt.assert_array_almost_equal(odf1, odf2)
# A boot sample with less sh coeffs should have residuals, thus the two
# should be different
boot_pmf_gen2 = BootPmfGen(data, model=csd_model, sphere=hsph_updated,
sh_order=4)
odf1 = boot_pmf_gen2.get_pmf(np.array([1.5, 1.5, 1.5]))
odf2 = boot_pmf_gen2.get_pmf(np.array([1.5, 1.5, 1.5]))
npt.assert_(np.any(odf1 != odf2))
# test with a gtab with two shells and assert you get an error
bvals[-1] = 2000
gtab = gradient_table(bvals, bvecs)
csd_model = ConstrainedSphericalDeconvModel(gtab, None, sh_order=6)
npt.assert_raises(ValueError, BootPmfGen, data, csd_model, hsph_updated, 6)
def test_boot_pmf():
# This tests the local model used for the bootstrapping.
hsph_updated = HemiSphere.from_sphere(unit_octahedron)
vertices = hsph_updated.vertices
bvecs = vertices
bvals = np.ones(len(vertices)) * 1000
bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)
bvals = np.insert(bvals, 0, 0)
gtab = gradient_table(bvals, bvecs)
voxel = single_tensor(gtab)
data = np.tile(voxel, (3, 3, 3, 1))
point = np.array([1., 1., 1.])
tensor_model = TensorModel(gtab)
boot_pmf_gen = BootPmfGen(data, model=tensor_model, sphere=hsph_updated)
no_boot_pmf = boot_pmf_gen.get_pmf_no_boot(point)
model_pmf = tensor_model.fit(voxel).odf(hsph_updated)
npt.assert_equal(len(hsph_updated.vertices), no_boot_pmf.shape[0])
npt.assert_array_almost_equal(no_boot_pmf, model_pmf)
# test model spherical harmonic order different than bootstrap order
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
# Tests that the first catched warning comes from
# the CSD model constructor
npt.assert_(issubclass(w[0].category, UserWarning))
npt.assert_("Number of parameters required " in str(w[0].message))
# Tests that additionnal warnings are raised for outdated SH basis
npt.assert_(len(w) > 1)
boot_pmf_gen_sh4 = BootPmfGen(data,
model=csd_model,
sphere=hsph_updated,
sh_order=4)
pmf_sh4 = boot_pmf_gen_sh4.get_pmf(point)
npt.assert_equal(len(hsph_updated.vertices), pmf_sh4.shape[0])
npt.assert_(np.sum(pmf_sh4.shape) > 0)
boot_pmf_gen_sh8 = BootPmfGen(data,
model=csd_model,
sphere=hsph_updated,
sh_order=8)
pmf_sh8 = boot_pmf_gen_sh8.get_pmf(point)
npt.assert_equal(len(hsph_updated.vertices), pmf_sh8.shape[0])
npt.assert_(np.sum(pmf_sh8.shape) > 0)
def test_boot_pmf():
"""This tests the local model used for the bootstrapping.
"""
hsph_updated = HemiSphere.from_sphere(unit_octahedron)
vertices = hsph_updated.vertices
bvecs = vertices
bvals = np.ones(len(vertices)) * 1000
bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)
bvals = np.insert(bvals, 0, 0)
gtab = gradient_table(bvals, bvecs)
voxel = single_tensor(gtab)
data = np.tile(voxel, (3, 3, 3, 1))
point = np.array([1., 1., 1.])
tensor_model = TensorModel(gtab)
boot_pmf_gen = BootPmfGen(data, model=tensor_model, sphere=hsph_updated)
no_boot_pmf = boot_pmf_gen.get_pmf_no_boot(point)
model_pmf = tensor_model.fit(voxel).odf(hsph_updated)
npt.assert_equal(len(hsph_updated.vertices), no_boot_pmf.shape[0])
npt.assert_array_almost_equal(no_boot_pmf, model_pmf)
# test model spherical harmonic order different than bootstrap order
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
csd_model = ConstrainedSphericalDeconvModel(gtab, None, sh_order=6)
assert_greater(
len([lw for lw in w if issubclass(lw.category, UserWarning)]), 0)
boot_pmf_gen_sh4 = BootPmfGen(data,
model=csd_model,
sphere=hsph_updated,
sh_order=4)
pmf_sh4 = boot_pmf_gen_sh4.get_pmf(point)
npt.assert_equal(len(hsph_updated.vertices), pmf_sh4.shape[0])
npt.assert_(np.sum(pmf_sh4.shape) > 0)
boot_pmf_gen_sh8 = BootPmfGen(data,
model=csd_model,
sphere=hsph_updated,
sh_order=8)
pmf_sh8 = boot_pmf_gen_sh8.get_pmf(point)
npt.assert_equal(len(hsph_updated.vertices), pmf_sh8.shape[0])
npt.assert_(np.sum(pmf_sh8.shape) > 0)
def test_boot_pmf():
"""This tests the local model used for the bootstrapping.
"""
hsph_updated = HemiSphere.from_sphere(unit_octahedron)
vertices = hsph_updated.vertices
bvecs = vertices
bvals = np.ones(len(vertices)) * 1000
bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)
bvals = np.insert(bvals, 0, 0)
gtab = gradient_table(bvals, bvecs)
voxel = single_tensor(gtab)
data = np.tile(voxel, (3, 3, 3, 1))
point = np.array([1., 1., 1.])
tensor_model = TensorModel(gtab)
boot_pmf_gen = BootPmfGen(data, model=tensor_model, sphere=hsph_updated)
no_boot_pmf = boot_pmf_gen.get_pmf_no_boot(point)
model_pmf = tensor_model.fit(voxel).odf(hsph_updated)
npt.assert_equal(len(hsph_updated.vertices), no_boot_pmf.shape[0])
npt.assert_array_almost_equal(no_boot_pmf, model_pmf)
# test model sherical harminic order different than bootstrap order
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
csd_model = ConstrainedSphericalDeconvModel(gtab, None, sh_order=6)
assert_greater(len([lw for lw in w if issubclass(lw.category,
UserWarning)]), 0)
boot_pmf_gen_sh4 = BootPmfGen(data, model=csd_model, sphere=hsph_updated,
sh_order=4)
pmf_sh4 = boot_pmf_gen_sh4.get_pmf(point)
npt.assert_equal(len(hsph_updated.vertices), pmf_sh4.shape[0])
npt.assert_(np.sum(pmf_sh4.shape) > 0)
boot_pmf_gen_sh8 = BootPmfGen(data, model=csd_model, sphere=hsph_updated,
sh_order=8)
pmf_sh8 = boot_pmf_gen_sh8.get_pmf(point)
npt.assert_equal(len(hsph_updated.vertices), pmf_sh8.shape[0])
npt.assert_(np.sum(pmf_sh8.shape) > 0)