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()
sph_io.savez('odf_coeffs_%03d' % k, beta=beta, kernel_N=kernel_N,
separation=gamma, weights=w,
mevecs=[R0, R1],
signal=E)
nnz = np.sum(beta != 0)
print 'Compression: %.2f%%' % ((len(beta) - nnz) / len(beta) * 100)
print 'Non-zero coefficients: %d/%d' % (nnz, len(beta))
print 'Error (Q-space):', np.linalg.norm(X.dot(beta) - y)
## beta[beta < 1e-5] = 0
# ==========================
# ODF-domain: Peak detection
# ==========================
if visualize_odf:
mask = (beta != 0)
plot.scatter_3D(theta_odf, phi_odf, color=(0, 0, 1))
plot.scatter_3D(theta_odf[mask], phi_odf[mask], 1 + beta[mask]/beta.max(),
transparent=True, color=(1, 0, 0), scale_mode='scalar',
scale_factor=0.1, opacity=0.7)
f1_theta, f1_phi, f1_r = coord.car2sph(*R0.dot([1, 0, 0]))
f2_theta, f2_phi, f2_r = coord.car2sph(*R1.dot([1, 0, 0]))
plot.scatter_3D(f1_theta, f1_phi, color=(0, 1, 0), scale_factor=0.15)
plot.scatter_3D(f2_theta, f2_phi, color=(0, 1, 0), scale_factor=0.15)
plot.show()
def weighted_kmeans(c_theta, c_phi, p_theta, p_phi, v):
D = 1 / (eps + sphere.arc_length(c_theta, c_phi,
p_theta[:, None], p_phi[:, None]))**2
# D = np.exp(-sphere.arc_length(c_theta, c_phi, p_theta[:, None], p_phi[:, None])**2)
D *= v[:, None]
D /= D.sum(axis=0)
P = np.column_stack((p_theta, p_phi))
C = (D[:, :, None] * P[:, None, :]).sum(axis=0)
return C[:, 0], C[:, 1]
for i in range(50):
c_theta, c_phi = weighted_kmeans(c_theta, c_phi, theta, phi, beta)
angles = np.rad2deg(np.arccos(sphere.cos_inc_angle(
c_theta, c_phi, c_theta[:, None], c_phi[:, None])))
angles[angles < 1e-3] = 0
print "True separation:", separation
print "Calculated:", angles
mask = (beta != 0)
plot.scatter_3D(theta, phi, color=(0, 0, 1))
plot.scatter_3D(theta[mask], phi[mask], 1 + beta[mask]/beta.max(),
transparent=True, color=(1, 0, 0), scale_mode='scalar',
scale_factor=0.1, opacity=0.7)
plot.scatter_3D(c_theta, c_phi, color=(1, 1, 0))
plot.show()
import sys sys.path.insert(0, '..') import numpy as np from sphdif import plot, coord from dipy.core.subdivide_octahedron import create_unit_sphere s = create_unit_sphere(3) from mayavi import mlab mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0)) mask = s.theta <= np.pi/2. plot.scatter_3D(s.theta[mask], s.phi[mask], color=(0, 0, 1)) #ef = s.edges.ravel() #for e in s.edges: # mlab.plot3d(s.x[e], s.y[e], s.z[e], tube_radius=None) N = 20 mlab.quiver3d([s.x[N]], [s.y[N]], [s.z[N]], color=(1, 0, 0), mode='2darrow') plot.show()
