难以捉摸：设置 pipeline.tube 半径
Mayavi : setting pipeline.tube radius
我正在使用 Mayavi 绘制 3D 网络，
edge_size = 0.2
pts = mlab.points3d(x, y, z,
scale_mode='none',
scale_factor=0.1)
pts.mlab_source.dataset.lines = np.array(graph.edges())
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
我想更改 edge/tube 半径。所以我尝试了
tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius)
我收到一个错误提示，
traits.trait_errors.TraitError: The 'tube_radius' trait of a TubeFactory instance must be a float
根据错误，我了解到无法将列表分配给 tube_radius。在这种情况下，我不确定如何为每条边分配不同的半径。
有关如何分配边 weights/edge 半径的任何建议都会有所帮助。
编辑：完整的工作示例
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from mayavi import mlab
def main(edge_color=(0.8, 0.8, 0.8), edge_size=0.02):
t = [1, 2, 3, 4, 5]
h = [2, 3, 4, 5, 6]
ed_ls = [(x, y) for x, y in zip(t, h)]
G = nx.OrderedGraph()
G.add_edges_from(ed_ls)
nx.draw(G)
plt.show()
graph_pos = nx.spring_layout(G, dim=3)
# numpy array of x,y,z positions in sorted node order
xyz = np.array([graph_pos[v] for v in sorted(G)])
mlab.figure(1)
mlab.clf()
pts = mlab.points3d(xyz[:, 0], xyz[:, 1], xyz[:, 2])
pts.mlab_source.dataset.lines = np.array(G.edges())
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
mlab.pipeline.surface(tube, color=edge_color)
mlab.show() # interactive window
main()
要在预期输出中添加的新边权重：
listofedgeradius = [1, 2, 3, 4, 5]
tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius)
在我看来，您不能同时绘制多个不同直径的管子。
所以一个解决方案是一个接一个地绘制它们：
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from mayavi import mlab
def main(edge_color=(0.8, 0.8, 0.8)):
t = [1, 2, 4, 4, 5, 3, 5]
h = [2, 3, 6, 5, 6, 4, 1]
ed_ls = [(x, y) for x, y in zip(t, h)]
G = nx.OrderedGraph()
G.add_edges_from(ed_ls)
graph_pos = nx.spring_layout(G, dim=3)
print(graph_pos)
# numpy array of x,y,z positions in sorted node order
xyz = np.array([graph_pos[v] for v in sorted(G)])
listofedgeradius = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) * 0.1
for i, e in enumerate(G.edges()):
# node number of the edge
i1, i2 = e
# graph_pos is a dictionary
c1 = graph_pos[i1]
c2 = graph_pos[i2]
edge_xyz = np.vstack((c1, c2))
pts = mlab.points3d(edge_xyz[:, 0], edge_xyz[:, 1], edge_xyz[:, 2])
#pts.mlab_source.dataset.lines = np.array(G.edges())
# always first and second point
pts.mlab_source.dataset.lines = np.array([[0, 1]])
tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius[i])
mlab.pipeline.surface(tube, color=edge_color)
mlab.gcf().scene.parallel_projection = True
mlab.show() # interactive window
main()
这是一个更大的例子，有 100 个边（下图），这个解决方案的一个警告变得很明显：for 循环很慢。
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from mayavi import mlab
def main(edge_color=(0.8, 0.8, 0.8)):
n = 100
t = np.random.randint(100, size=n)
h = np.random.randint(100, size=n)
ed_ls = [(x, y) for x, y in zip(t, h)]
G = nx.OrderedGraph()
G.add_edges_from(ed_ls)
graph_pos = nx.spring_layout(G, dim=3)
print(graph_pos)
# numpy array of x,y,z positions in sorted node order
xyz = np.array([graph_pos[v] for v in sorted(G)])
listofedgeradius = np.random.rand(n) * 0.01
for i, e in enumerate(G.edges()):
print(i)
# node number of the edge
i1, i2 = e
# graph_pos is a dictionary
c1 = graph_pos[i1]
c2 = graph_pos[i2]
edge_xyz = np.vstack((c1, c2))
pts = mlab.points3d(edge_xyz[:, 0], edge_xyz[:, 1], edge_xyz[:, 2])
#pts.mlab_source.dataset.lines = np.array(G.edges())
# always first and second point
pts.mlab_source.dataset.lines = np.array([[0, 1]])
tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius[i])
mlab.pipeline.surface(tube, color=edge_color)
mlab.gcf().scene.parallel_projection = True
mlab.show() # interactive window
main()
受到 this, and this 的启发，我整理了第一个适用于大型图的示例（我尝试了多达 5000 个边）。还有一个for循环，但它不是用来绘图的，只是用来收集numpy数组中的数据，所以还不错。
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from mayavi import mlab
def main(edge_color=(0.8, 0.8, 0.8)):
n = 5000
t = np.random.randint(100, size=n)
h = np.random.randint(100, size=n)
ed_ls = [(x, y) for x, y in zip(t, h)]
G = nx.OrderedGraph()
G.add_edges_from(ed_ls)
graph_pos = nx.spring_layout(G, dim=3)
print(graph_pos)
listofedgeradius = np.random.rand(n) * 0.01
# We create a list of positions and connections, each describing a line.
# We will collapse them in one array before plotting.
x = list()
y = list()
z = list()
s = list()
connections = list()
N = 2 # every edge brings two nodes
# The index of the current point in the total amount of points
index = 0
for i, e in enumerate(G.edges()):
# node number of the edge
i1, i2 = e
# graph_pos is a dictionary
c1 = graph_pos[i1]
c2 = graph_pos[i2]
edge_xyz = np.vstack((c1, c2))
x.append(edge_xyz[:, 0])
y.append(edge_xyz[:, 1])
z.append(edge_xyz[:, 2])
s.append(listofedgeradius[i])
s.append(listofedgeradius[i])
# This is the tricky part: in a line, each point is connected
# to the one following it. We have to express this with the indices
# of the final set of points once all lines have been combined
# together, this is why we need to keep track of the total number of
# points already created (index)
ics = np.vstack(
[np.arange(index, index + N - 1.5),
np.arange(index + 1, index + N - .5)]
).T
#print(ics)
connections.append(ics)
index += N
# Now collapse all positions, scalars and connections in big arrays
x = np.hstack(x)
y = np.hstack(y)
z = np.hstack(z)
s = np.hstack(s)
# print(x.shape)
# print(y.shape)
# print(z.shape)
# print(s.shape)
connections = np.vstack(connections)
# # graph_pos is a dictionary
# c1 = graph_pos[i1]
# c2 = graph_pos[i2]
# edge_xyz = np.vstack((c1, c2))
#src = mlab.points3d(x, y, z, s)
#src = mlab.pipeline.scalar_scatter(x, y, z, s)
src = mlab.plot3d(x, y, z, s)
print(src)
print(src.parent)
print(src.parent.parent)
#src.parent.parent.filter.vary_radius = 'vary_radius_by_scalar'
src.parent.parent.filter.vary_radius = 'vary_radius_by_absolute_scalar'
# Connect them
src.mlab_source.dataset.lines = connections
#src.update()
# The stripper filter cleans up connected lines
lines = mlab.pipeline.stripper(src)
# Finally, display the set of lines
#mlab.pipeline.surface(lines, colormap='Accent', line_width=1, opacity=.4)
#tube = mlab.pipeline.tube(src, tube_radius=0.01)
#tube.filter.radius_factor = 1
#tube.filter.vary_radius = 'vary_radius_by_scalar'
#surf = mlab.pipeline.surface(tube, opacity=0.6, color=(0.8,0.8,0))
#t = mlab.plot3d(x, y, z, s, tube_radius=10)
#t.parent.parent.filter.vary_radius = 'vary_radius_by_scalar'
#pts.mlab_source.dataset.lines = np.array(G.edges())
# always first and second point
#pts.mlab_source.dataset.lines = np.array([[0, 1]])
#tube = mlab.pipeline.tube(src, tube_radius=listofedgeradius[i])
#mlab.pipeline.surface(tube, color=edge_color)
# pts = self.scene.mlab.quiver3d(x, y, z, atomsScales, v, w,
# scalars=scalars, mode='sphere', vmin=0.0, vmax=1.0, figure = scene)
# pts.mlab_source.dataset.lines = bonds
# tube = scene.mlab.pipeline.tube(pts, tube_radius=0.01)
# tube.filter.radius_factor = 1
# tube.filter.vary_radius = 'vary_radius_by_scalar'
# surf = scene.mlab.pipeline.surface(tube, opacity=0.6, color=(0.8,0.8,0))
# t = mlab.plot3d(x, y, z, s, tube_radius=10)
#t.parent.parent.filter.vary_radius = 'vary_radius_by_scalar'
# self.plot = self.scene.mlab.plot3d(x, y, z, t,
# tube_radius=self.radius, colormap='Spectral')
# else:
# self.plot.parent.parent.filter.radius = self.radius
mlab.gcf().scene.parallel_projection = True
# And choose a nice view
mlab.view(33.6, 106, 5.5, [0, 0, .05])
mlab.roll(125)
mlab.show()
main()
我正在使用 Mayavi 绘制 3D 网络，
edge_size = 0.2
pts = mlab.points3d(x, y, z,
scale_mode='none',
scale_factor=0.1)
pts.mlab_source.dataset.lines = np.array(graph.edges())
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
我想更改 edge/tube 半径。所以我尝试了
tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius)
我收到一个错误提示，
traits.trait_errors.TraitError: The 'tube_radius' trait of a TubeFactory instance must be a float
根据错误，我了解到无法将列表分配给 tube_radius。在这种情况下，我不确定如何为每条边分配不同的半径。
有关如何分配边 weights/edge 半径的任何建议都会有所帮助。
编辑：完整的工作示例
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from mayavi import mlab
def main(edge_color=(0.8, 0.8, 0.8), edge_size=0.02):
t = [1, 2, 3, 4, 5]
h = [2, 3, 4, 5, 6]
ed_ls = [(x, y) for x, y in zip(t, h)]
G = nx.OrderedGraph()
G.add_edges_from(ed_ls)
nx.draw(G)
plt.show()
graph_pos = nx.spring_layout(G, dim=3)
# numpy array of x,y,z positions in sorted node order
xyz = np.array([graph_pos[v] for v in sorted(G)])
mlab.figure(1)
mlab.clf()
pts = mlab.points3d(xyz[:, 0], xyz[:, 1], xyz[:, 2])
pts.mlab_source.dataset.lines = np.array(G.edges())
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
mlab.pipeline.surface(tube, color=edge_color)
mlab.show() # interactive window
main()
要在预期输出中添加的新边权重：
listofedgeradius = [1, 2, 3, 4, 5]
tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius)
在我看来，您不能同时绘制多个不同直径的管子。 所以一个解决方案是一个接一个地绘制它们：
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from mayavi import mlab
def main(edge_color=(0.8, 0.8, 0.8)):
t = [1, 2, 4, 4, 5, 3, 5]
h = [2, 3, 6, 5, 6, 4, 1]
ed_ls = [(x, y) for x, y in zip(t, h)]
G = nx.OrderedGraph()
G.add_edges_from(ed_ls)
graph_pos = nx.spring_layout(G, dim=3)
print(graph_pos)
# numpy array of x,y,z positions in sorted node order
xyz = np.array([graph_pos[v] for v in sorted(G)])
listofedgeradius = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) * 0.1
for i, e in enumerate(G.edges()):
# node number of the edge
i1, i2 = e
# graph_pos is a dictionary
c1 = graph_pos[i1]
c2 = graph_pos[i2]
edge_xyz = np.vstack((c1, c2))
pts = mlab.points3d(edge_xyz[:, 0], edge_xyz[:, 1], edge_xyz[:, 2])
#pts.mlab_source.dataset.lines = np.array(G.edges())
# always first and second point
pts.mlab_source.dataset.lines = np.array([[0, 1]])
tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius[i])
mlab.pipeline.surface(tube, color=edge_color)
mlab.gcf().scene.parallel_projection = True
mlab.show() # interactive window
main()
这是一个更大的例子，有 100 个边（下图），这个解决方案的一个警告变得很明显：for 循环很慢。
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from mayavi import mlab
def main(edge_color=(0.8, 0.8, 0.8)):
n = 100
t = np.random.randint(100, size=n)
h = np.random.randint(100, size=n)
ed_ls = [(x, y) for x, y in zip(t, h)]
G = nx.OrderedGraph()
G.add_edges_from(ed_ls)
graph_pos = nx.spring_layout(G, dim=3)
print(graph_pos)
# numpy array of x,y,z positions in sorted node order
xyz = np.array([graph_pos[v] for v in sorted(G)])
listofedgeradius = np.random.rand(n) * 0.01
for i, e in enumerate(G.edges()):
print(i)
# node number of the edge
i1, i2 = e
# graph_pos is a dictionary
c1 = graph_pos[i1]
c2 = graph_pos[i2]
edge_xyz = np.vstack((c1, c2))
pts = mlab.points3d(edge_xyz[:, 0], edge_xyz[:, 1], edge_xyz[:, 2])
#pts.mlab_source.dataset.lines = np.array(G.edges())
# always first and second point
pts.mlab_source.dataset.lines = np.array([[0, 1]])
tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius[i])
mlab.pipeline.surface(tube, color=edge_color)
mlab.gcf().scene.parallel_projection = True
mlab.show() # interactive window
main()
受到 this,
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from mayavi import mlab
def main(edge_color=(0.8, 0.8, 0.8)):
n = 5000
t = np.random.randint(100, size=n)
h = np.random.randint(100, size=n)
ed_ls = [(x, y) for x, y in zip(t, h)]
G = nx.OrderedGraph()
G.add_edges_from(ed_ls)
graph_pos = nx.spring_layout(G, dim=3)
print(graph_pos)
listofedgeradius = np.random.rand(n) * 0.01
# We create a list of positions and connections, each describing a line.
# We will collapse them in one array before plotting.
x = list()
y = list()
z = list()
s = list()
connections = list()
N = 2 # every edge brings two nodes
# The index of the current point in the total amount of points
index = 0
for i, e in enumerate(G.edges()):
# node number of the edge
i1, i2 = e
# graph_pos is a dictionary
c1 = graph_pos[i1]
c2 = graph_pos[i2]
edge_xyz = np.vstack((c1, c2))
x.append(edge_xyz[:, 0])
y.append(edge_xyz[:, 1])
z.append(edge_xyz[:, 2])
s.append(listofedgeradius[i])
s.append(listofedgeradius[i])
# This is the tricky part: in a line, each point is connected
# to the one following it. We have to express this with the indices
# of the final set of points once all lines have been combined
# together, this is why we need to keep track of the total number of
# points already created (index)
ics = np.vstack(
[np.arange(index, index + N - 1.5),
np.arange(index + 1, index + N - .5)]
).T
#print(ics)
connections.append(ics)
index += N
# Now collapse all positions, scalars and connections in big arrays
x = np.hstack(x)
y = np.hstack(y)
z = np.hstack(z)
s = np.hstack(s)
# print(x.shape)
# print(y.shape)
# print(z.shape)
# print(s.shape)
connections = np.vstack(connections)
# # graph_pos is a dictionary
# c1 = graph_pos[i1]
# c2 = graph_pos[i2]
# edge_xyz = np.vstack((c1, c2))
#src = mlab.points3d(x, y, z, s)
#src = mlab.pipeline.scalar_scatter(x, y, z, s)
src = mlab.plot3d(x, y, z, s)
print(src)
print(src.parent)
print(src.parent.parent)
#src.parent.parent.filter.vary_radius = 'vary_radius_by_scalar'
src.parent.parent.filter.vary_radius = 'vary_radius_by_absolute_scalar'
# Connect them
src.mlab_source.dataset.lines = connections
#src.update()
# The stripper filter cleans up connected lines
lines = mlab.pipeline.stripper(src)
# Finally, display the set of lines
#mlab.pipeline.surface(lines, colormap='Accent', line_width=1, opacity=.4)
#tube = mlab.pipeline.tube(src, tube_radius=0.01)
#tube.filter.radius_factor = 1
#tube.filter.vary_radius = 'vary_radius_by_scalar'
#surf = mlab.pipeline.surface(tube, opacity=0.6, color=(0.8,0.8,0))
#t = mlab.plot3d(x, y, z, s, tube_radius=10)
#t.parent.parent.filter.vary_radius = 'vary_radius_by_scalar'
#pts.mlab_source.dataset.lines = np.array(G.edges())
# always first and second point
#pts.mlab_source.dataset.lines = np.array([[0, 1]])
#tube = mlab.pipeline.tube(src, tube_radius=listofedgeradius[i])
#mlab.pipeline.surface(tube, color=edge_color)
# pts = self.scene.mlab.quiver3d(x, y, z, atomsScales, v, w,
# scalars=scalars, mode='sphere', vmin=0.0, vmax=1.0, figure = scene)
# pts.mlab_source.dataset.lines = bonds
# tube = scene.mlab.pipeline.tube(pts, tube_radius=0.01)
# tube.filter.radius_factor = 1
# tube.filter.vary_radius = 'vary_radius_by_scalar'
# surf = scene.mlab.pipeline.surface(tube, opacity=0.6, color=(0.8,0.8,0))
# t = mlab.plot3d(x, y, z, s, tube_radius=10)
#t.parent.parent.filter.vary_radius = 'vary_radius_by_scalar'
# self.plot = self.scene.mlab.plot3d(x, y, z, t,
# tube_radius=self.radius, colormap='Spectral')
# else:
# self.plot.parent.parent.filter.radius = self.radius
mlab.gcf().scene.parallel_projection = True
# And choose a nice view
mlab.view(33.6, 106, 5.5, [0, 0, .05])
mlab.roll(125)
mlab.show()
main()