Deep learning for NeuroImaging in Python.
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Spherical neighbors¶
Credit: A Grigis
A simple example on how to build spherical neighbors using an icosahedron.
import matplotlib.pyplot as plt
from surfify.utils import icosahedron, neighbors, get_rectangular_projection
from surfify.plotting import plot_trisurf
Direct Neighbor¶
Display direct neighbors for some vertices.
vertices, triangles = icosahedron(order=2)
neighs = neighbors(vertices, triangles, depth=3, direct_neighbor=True)
fig, ax = plt.subplots(1, 1, subplot_kw={
"projection": "3d", "aspect": "auto"}, figsize=(10, 10))
plot_trisurf(vertices, triangles=triangles, colorbar=False, fig=fig, ax=ax,
linewidths=0.3)
for vidx in (4, 40):
for cnt, idx in enumerate(neighs[vidx]):
point = vertices[idx]
ax.scatter(point[0], point[1], point[2], marker="o", s=100)
ax.text(point[0], point[1], point[2], str(cnt), size=20)
Rectagular Tangent Plane Neighbor¶
Display 3x3 rectangular tangent plane neighbors for some vertices and the associated projection on the sphere.
zoom = 2
colors = ["red", "green", "blue", "orange", "purple", "brown", "pink",
"gray", "olive", "cyan", "yellow", "tan", "salmon", "violet",
"steelblue", "lime", "navy"] * 5
fig, ax = plt.subplots(1, 1, subplot_kw={
"projection": "3d", "aspect": "auto"}, figsize=(10, 10))
plot_trisurf(vertices, triangles=triangles, fig=fig, ax=ax)
for idx in (13, 40):
node = vertices[idx]
node_rec_neighs, node_tplane_neighs = get_rectangular_projection(
node, size=3, zoom=zoom)
ax.scatter(node[0], node[1], node[2], marker="^", c=colors[0], s=100)
ax.text(point[0], point[1], point[2], 0, size=20)
for cnt, point in enumerate(node_tplane_neighs):
ax.scatter(point[0], point[1], point[2], marker="o", c=colors[cnt + 1],
s=100)
ax.text(point[0], point[1], point[2], str(cnt + 1), size=20)
for cnt, point in enumerate(node_rec_neighs):
ax.scatter(point[0], point[1], point[2], marker="X", c=colors[cnt + 1],
s=100)
ax.text(point[0], point[1], point[2], str(cnt + 1), size=20)
plt.show()
Total running time of the script: (0 minutes 2.207 seconds)
Estimated memory usage: 197 MB
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