def show_coverage_2d(rg, field, rm=None, projection='moll',
vmin=None, markersize=30, ax=None):
"""Show field coverage by projecting the sphere onto a flat 2d surface.
Parameters
----------
rg : (M, 3) ndarray
Gradient directions in Cartesian coordinates.
field : (M, N) ndarray
Field values on a grid. The grid is on ``theta = [0, pi]`` and
``phi = [0, 2 * pi]``.
rm : (M, 3) ndarray
Missing / dropped gradient directions.
projection : 'moll', 'ortho', etc.
Basemap projection.
"""
import matplotlib.pyplot as plt
npts_lat, npts_lon = field.shape
theta = np.linspace(0, np.pi, npts_lat)
phi = np.linspace(0, 2 * np.pi, npts_lon)
m = plot.surf_grid(field, theta, phi,
projection=projection, vmin=vmin, ax=ax)
plt.colorbar(orientation='horizontal', format='%.2g', ax=ax)
x, y, z = rg
theta, phi, rho = coord.car2sph(x, y, z)
plot.scatter(theta, phi, basemap=m, color='g', s=markersize, ax=ax)
if rm is not None and rm.shape[1] > 0:
x, y, z = rm
theta, phi, rho = coord.car2sph(x, y, z)
plot.scatter(theta, phi, basemap=m, color='r', s=markersize, ax=ax)
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()
def test_car2sph():
theta, phi, r = car2sph(1, 0, 0)
assert_almost_equal(r, 1, decimal=5)
assert_almost_equal(theta, np.pi / 2, decimal=5)
assert_almost_equal(phi, 0, decimal=5)
theta, phi, r = car2sph(0, 1, 0)
assert_almost_equal(r, 1, decimal=5)
assert_almost_equal(theta, np.pi / 2, decimal=5)
assert_almost_equal(phi, np.pi / 2, decimal=5)
from dipy.reconst.shm import SlowAdcOpdfModel, MonoExpOpdfModel, QballOdfModel
coords = sph_io.load('sphere_pts.npz')
#from dipy.data import get_sphere
#verts, faces = get_sphere('symmetric724')
## from dipy.core.triangle_subdivide import create_unit_sphere
## verts, edges, sides = create_unit_sphere(5)
## faces = edges[sides, 0]
from dipy.core.triangle_subdivide import create_unit_sphere
verts, edges, sides = create_unit_sphere(5)
faces = edges[sides, 0]
theta, phi, r = car2sph(*verts.T)
sampling_xyz = np.column_stack(
sph2car(coords['gradient_theta'], coords['gradient_phi'])
)
## verts = np.column_stack(sph2car(theta, phi))
## from dipy.core.meshes import faces_from_vertices
## faces = faces_from_vertices(verts)
def separation_from_odf(odf):
# Find angles
from dipy.reconst.recspeed import local_maxima
p, i = local_maxima(odf, edges)
p = p[:2]
theta72, phi72, w72 = sphere.quadrature_points(N=72)
theta132, phi132, w132 = sphere.quadrature_points(N=132)
#theta492, phi492, w492 = sphere.quadrature_points(N=492)
theta, phi = theta72, phi72
from dipy.data import get_data
img, bvals, bvecs = get_data('small_64D')
b = np.load(bvals)
bvecs = np.load(bvecs)
bvecs = bvecs[b > 0]
b = b[b > 0]
theta, phi, _ = coord.car2sph(*bvecs.T)
theta_odf, phi_odf = theta132, phi132
kernel = inv_funk_radon_even_kernel
kernel_N = 8
X = kernel_matrix(theta, phi, theta_odf, phi_odf, kernel=kernel, N=kernel_N)
D = 150 # Grid density for plots (higher => more dense)
b = 3000 + np.random.normal(scale=4, size=len(theta)) # Make up somewhat realistic b-values
xyz = np.column_stack(coord.sph2car(theta, phi))
sph_io.savez('sphere_pts', gradient_theta=theta, gradient_phi=phi,
odf_theta=theta_odf, odf_phi=phi_odf, b=b)
# Fiber weights
