Ejemplo n.º 1
0
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)
Ejemplo n.º 2
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    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
Ejemplo n.º 3
0
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)
Ejemplo n.º 4
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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)
Ejemplo n.º 5
0
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)
Ejemplo n.º 6
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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)
Ejemplo n.º 7
0
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,