def test_hat_and_lcr():
hemi = hemi_icosahedron.subdivide(3)
m, n = sph_harm_ind_list(8)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
B = real_sh_descoteaux_from_index(m, n, hemi.theta[:, None],
hemi.phi[:, None])
H = hat(B)
B_hat = np.dot(H, B)
assert_array_almost_equal(B, B_hat)
R = lcr_matrix(H)
d = np.arange(len(hemi.theta))
r = d - np.dot(H, d)
lev = np.sqrt(1 - H.diagonal())
r /= lev
r -= r.mean()
r2 = np.dot(R, d)
assert_array_almost_equal(r, r2)
r3 = np.dot(d, R.T)
assert_array_almost_equal(r, r3)
def __init__(self, data, model, sphere, sh_order=None, tol=1e-2):
if sh_order is None:
if hasattr(model, "sh_order"):
sh_order = model.sh_order
else:
sh_order = default_SH
self.where_dwi = shm.lazy_index(~model.gtab.b0s_mask)
if not isinstance(self.where_dwi, slice):
msg = ("For optimal bootstrap tracking consider reordering the "
"diffusion volumes so that all the b0 volumes are at the "
"beginning")
warn(msg)
x, y, z = model.gtab.gradients[self.where_dwi].T
r, theta, phi = cart2sphere(x, y, z)
b_range = (r.max() - r.min()) / r.min()
if b_range > tol:
raise ValueError("BootOdfGen only supports single shell data")
B, m, n = shm.real_sym_sh_basis(sh_order, theta, phi)
H = shm.hat(B)
R = shm.lcr_matrix(H)
self.data = np.asarray(data, "float64")
self.model = model
self.sphere = sphere
self.H = H
self.R = R
def test_hat_and_lcr():
hemi = hemi_icosahedron.subdivide(3)
m, n = sph_harm_ind_list(8)
B = real_sph_harm(m, n, hemi.theta[:, None], hemi.phi[:, None])
H = hat(B)
B_hat = np.dot(H, B)
assert_array_almost_equal(B, B_hat)
R = lcr_matrix(H)
d = np.arange(len(hemi.theta))
r = d - np.dot(H, d)
lev = np.sqrt(1 - H.diagonal())
r /= lev
r -= r.mean()
r2 = np.dot(R, d)
assert_array_almost_equal(r, r2)
r3 = np.dot(d, R.T)
assert_array_almost_equal(r, r3)
def test_hat_and_lcr():
hemi = hemi_icosahedron.subdivide(3)
m, n = sph_harm_ind_list(8)
B = real_sph_harm(m, n, hemi.theta[:, None], hemi.phi[:, None])
H = hat(B)
B_hat = np.dot(H, B)
assert_array_almost_equal(B, B_hat)
R = lcr_matrix(H)
d = np.arange(len(hemi.theta))
r = d - np.dot(H, d)
lev = np.sqrt(1 - H.diagonal())
r /= lev
r -= r.mean()
r2 = np.dot(R, d)
assert_array_almost_equal(r, r2)
r3 = np.dot(d, R.T)
assert_array_almost_equal(r, r3)
def test_hat_and_lcr():
v, e, f = create_half_unit_sphere(6)
m, n = sph_harm_ind_list(8)
r, pol, azi = cart2sphere(*v.T)
B = real_sph_harm(m, n, azi[:, None], pol[:, None])
H = hat(B)
B_hat = np.dot(H, B)
assert_array_almost_equal(B, B_hat)
R = lcr_matrix(H)
d = np.arange(len(azi))
r = d - np.dot(H, d)
lev = np.sqrt(1 - H.diagonal())
r /= lev
r -= r.mean()
r2 = np.dot(R, d)
assert_array_almost_equal(r, r2)
r3 = np.dot(d, R.T)
assert_array_almost_equal(r, r3)
def test_hat_and_lcr():
v, e, f = create_half_unit_sphere(6)
m, n = sph_harm_ind_list(8)
r, pol, azi = cart2sphere(*v.T)
B = real_sph_harm(m, n, azi[:, None], pol[:, None])
H = hat(B)
B_hat = np.dot(H, B)
assert_array_almost_equal(B, B_hat)
R = lcr_matrix(H)
d = np.arange(len(azi))
r = d - np.dot(H, d)
lev = np.sqrt(1 - H.diagonal())
r /= lev
r -= r.mean()
r2 = np.dot(R, d)
assert_array_almost_equal(r, r2)
r3 = np.dot(d, R.T)
assert_array_almost_equal(r, r3)
delta_b, delta_q = fit_matrix
# setup sampling matrix
sphere = small_sphere
theta = sphere.theta
phi = sphere.phi
sampling_matrix, _, _ = shm.real_sym_sh_basis(sh_order, theta, phi)
## from BootPmfGen __init__
# setup H and R matrices
# TODO: figure out how to get H, R matrices from direction getter object
x, y, z = model.gtab.gradients[dwi_mask].T
r, theta, phi = shm.cart2sphere(x, y, z)
B, _, _ = shm.real_sym_sh_basis(sh_order, theta, phi)
H = shm.hat(B)
R = shm.lcr_matrix(H)
# create floating point copy of data
dataf = np.asarray(data, dtype=float)
print('streamline gen')
global_chunk_size = args.chunk_size * args.ngpus
nchunks = (seed_mask.shape[0] + global_chunk_size - 1) // global_chunk_size
#streamline_generator = LocalTracking(boot_dg, tissue_classifier, seed_mask, affine=np.eye(4), step_size=.5)
gpu_tracker = cuslines.GPUTracker(args.max_angle,
args.min_signal,
args.tc_threshold,
args.step_size,
dataf,
H,
