Mayavi：设置管道的半径

Question

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)

我想更改边/管的半径。所以我尝试过

tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius)

我看到一个错误，

traits.trait_errors.TraitError: The 'tube_radius' trait of a TubeFactory instance must be a float

根据错误，我知道无法将列表分配给tube_radius。在这种情况下，我不确定如何为每个边缘分配不同的半径。

关于如何分配边缘权重/边缘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缘权重：

   listofedgeradius = [1, 2, 3, 4, 5]
   tube = mlab.pipeline.tube(pts, tube_radius=listofedgeradius)

Answer 1

在我看来，您无法一次绘制多个直径不同的管。因此，一种解决方案是将它们一个接一个地绘制：

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()

这是一个较大的示例，具有100条边（下图），这种解决方案的一个明显警告是：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this，this和this的启发，我整理了第一个示例，该示例非常适合大型图形（我尝试了多达5000条边）。仍然有一个for循环，但它不用于绘图，仅用于收集numpy数组中的数据，因此还不错。

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()