def show_coverage_3d(rg, field, rm=None, vmin=None, sphere_colormap='jet'):
"""
"""
# Resolution with which the small spheres are drawn
pt_resolution = 20
# Size factor for small spheres
scale_factor = 0.1
# missing directions in red, if any
if rm is not None:
xm, ym, zm = rm
theta, phi, rho = coord.car2sph(xm, ym, zm)
plot.scatter_3D(theta, phi, resolution=pt_resolution,
scale_factor=scale_factor, name='Bad direction')
# 'good' directions in green
xg, yg, zg = rg
theta, phi, rho = coord.car2sph(xg, yg, zg)
# As simple spheres
plot.scatter_3D(theta, phi, color=(0, 1, 0), scale_factor=scale_factor,
resolution=pt_resolution, name='Good directions')
npts_lat, npts_lon = field.shape
theta = np.linspace(0, np.pi, npts_lat)
phi = np.linspace(0, 2 * np.pi, npts_lon)
s = plot.surf_grid_3D(field, theta, phi, vmin=vmin, name='Coverage')
mlab = plot.get_mlab()
mlab.colorbar(s, orientation='horizontal')
mlab.show()
sphere = create_unit_sphere(6)
bb = np.ones(len(sphere.vertices)) * b.mean()
E_ = w[0] * single_tensor(gradients=sphere.vertices, bvals=bb, S0=1, rotation=R0, SNR=SNR)
E_ += w[1] * single_tensor(gradients=sphere.vertices, bvals=bb, S0=1, rotation=R1, SNR=SNR)
ODF = w[0] * single_tensor_ODF(sphere.vertices, rotation=R0)
ODF += w[1] * single_tensor_ODF(sphere.vertices, rotation=R1)
from dipy.viz import show_odfs
show_odfs([[[E_, ODF]]], (sphere.vertices, sphere.faces))
if visualize_odf:
plot_ODF(grid_density=D)
mlab = plot.get_mlab()
mlab.show()
# ===========================-=====
# ODF-domain: Sparse reconstruction
# =================================
# Minimising the L1 penalized system
#
# ||Xb - y||_2^2 + lambda ||x||_1 subject to x_i >= 0.
#
# Here, X is reproducing Q-space kernel, y is the Q-space signal vector
# and b are the coefficents.
#
# Note that the L1 penalization of b forces it to be sparse. This also implies
# that the ODF-domain signal is sparse, since the kernels in A (the reproducing
"""Visualize inverse Funk-Radon transform elements with mayavi. """ from sphdif.kernel import kernel_plot, inv_funk_radon_even_kernel from sphdif.plot import get_mlab mlab = get_mlab() mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0)) kernel_plot(inv_funk_radon_even_kernel, N=14) mlab.show()
