将 Networkx 中生成的 3D 图形保存为 VTK 格式，以便在 Paraview 中查看

Question

我使用以下代码生成了一个 3D 图形网络，并使用 Mayavi 进行可视化。

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import networkx as nx
from mayavi import mlab


pos = [[0.1, 2, 0.3], [40, 0.5, -10],
       [0.1, -40, 0.3], [-49, 0.1, 2],
       [10.3, 0.3, 0.4], [-109, 0.3, 0.4]]
pos = pd.DataFrame(pos, columns=['x', 'y', 'z'])

ed_ls = [(x, y) for x, y in zip(range(0, 5), range(1, 6))]

G = nx.Graph()
G.add_edges_from(ed_ls)

nx.draw(G)
plt.show()


# plot 3D in mayavi
edge_size = 0.2
edge_color = (0.8, 0.8, 0.8)
bgcolor = (0, 0, 0)


mlab.figure(1, bgcolor=bgcolor)
mlab.clf()

for i, e in enumerate(G.edges()):
    # ----------------------------------------------------------------------------
    # the x,y, and z co-ordinates are here
    pts = mlab.points3d(pos['x'], pos['y'], pos['z'],
                        scale_mode='none',
                        scale_factor=1)
    # ----------------------------------------------------------------------------
    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()

我想就如何以 VTK 格式保存这个 3D 图形/如何 将 Networkx 图形对象转换为 VTK 文件，以便在 Paraview 中进行可视化。

编辑： 我已经尝试为输入 Networkx 图调整可用的代码 here 以上分享。但是，我无法获得输出。我只是得到一个空窗口，并且 vtkpolyData 没有绘制在窗口中。

"""
This code converts netwrokx graph to vtk polyData
ref: https://networkx.github.io/documentation/networkx-0.37/networkx.drawing.nx_vtk-pysrc.html
"""

import vtk
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt

from vtk.util.colors import banana, plum


def draw_nxvtk(G, node_pos):
    """
    Draw networkx graph in 3d with nodes at node_pos.

    See layout.py for functions that compute node positions.

    node_pos is a dictionary keyed by vertex with a three-tuple
    of x-y positions as the value.

    The node color is plum.
    The edge color is banana.

    All the nodes are the same size.

    """
    # set node positions
    np={}
    for n in G.nodes():
       try:
           np[n]=node_pos[n]
       except nx.NetworkXError:
           print("node %s doesn't have position"%n)

    nodePoints = vtk.vtkPoints()

    i=0
    for (x,y,z) in np.values():
       nodePoints.InsertPoint(i, x, y, z)
       i=i+1

    # Create a polydata to be glyphed.
    inputData = vtk.vtkPolyData()
    inputData.SetPoints(nodePoints)

    # Use sphere as glyph source.
    balls = vtk.vtkSphereSource()
    balls.SetRadius(.05)
    balls.SetPhiResolution(20)
    balls.SetThetaResolution(20)

    glyphPoints = vtk.vtkGlyph3D()
    glyphPoints.SetInputData(inputData)
    glyphPoints.SetSourceData(balls.GetOutput())

    glyphMapper = vtk.vtkPolyDataMapper()
    glyphMapper.SetInputData(glyphPoints.GetOutput())

    glyph = vtk.vtkActor()
    glyph.SetMapper(glyphMapper)
    glyph.GetProperty().SetDiffuseColor(plum)
    glyph.GetProperty().SetSpecular(.3)
    glyph.GetProperty().SetSpecularPower(30)

    # Generate the polyline for the spline.
    points = vtk.vtkPoints()
    edgeData = vtk.vtkPolyData()

    # Edges

    lines = vtk.vtkCellArray()
    i = 0
    for e in G.edges():
        # The edge e can be a 2-tuple (Graph) or a 3-tuple (Xgraph)
        u = e[0]
        v = e[1]
        if v in node_pos and u in node_pos:
            lines.InsertNextCell(2)
            for n in (u, v):
                (x, y, z) = node_pos[n]
                points.InsertPoint(i, x, y, z)
                lines.InsertCellPoint(i)
                i = i+1

    edgeData.SetPoints(points)
    edgeData.SetLines(lines)

    # Add thickness to the resulting line.
    Tubes = vtk.vtkTubeFilter()
    Tubes.SetNumberOfSides(16)
    Tubes.SetInputData(edgeData)
    Tubes.SetRadius(.01)
    #
    profileMapper = vtk.vtkPolyDataMapper()
    profileMapper.SetInputData(Tubes.GetOutput())

    #
    profile = vtk.vtkActor()
    profile.SetMapper(profileMapper)
    profile.GetProperty().SetDiffuseColor(banana)
    profile.GetProperty().SetSpecular(.3)
    profile.GetProperty().SetSpecularPower(30)

    # Now create the RenderWindow, Renderer and Interactor
    ren = vtk.vtkRenderer()
    renWin = vtk.vtkRenderWindow()
    renWin.AddRenderer(ren)

    iren = vtk.vtkRenderWindowInteractor()
    iren.SetRenderWindow(renWin)

    # Add the actors
    ren.AddActor(glyph)
    ren.AddActor(profile)

    renWin.SetSize(640, 640)

    iren.Initialize()
    renWin.Render()
    iren.Start()


if __name__ == "__main__":

    pos = [[0.1, 2, 0.3], [40, 0.5, -10],
           [0.1, -40, 0.3], [-49, 0.1, 2],
           [10.3, 0.3, 0.4], [-109, 0.3, 0.4]]
    pos = pd.DataFrame(pos, columns=['x', 'y', 'z'])
    pos_d = pos.T.to_dict(orient='list')
    
    ed_ls = [(x, y) for x, y in zip(range(0, 5), range(1, 6))]

    G = nx.Graph()
    G.add_edges_from(ed_ls)
    # nx.draw(G, with_labels=True, pos=nx.spring_layout(G))
    # plt.show()
    draw_nxvtk(G=G, node_pos=pos_d)

关于如何在运行上述代码时查看显示的 polyData 输出以及如何保存 vtkPolyData 以在 Paraview 中导入的建议将非常有帮助。

Answer 1

如果您可以使用构建在 vtk 之上的 vedo，这将变得很容易：

import networkx as nx

pos = [[0.1, 2, 0.3],    [40, 0.5, -10],
       [0.1, -40, 0.3],  [-49, 0.1, 2],
       [10.3, 0.3, 0.4], [-109, 0.3, 0.4]]

ed_ls = [(x, y) for x, y in zip(range(0, 5), range(1, 6))]

G = nx.Graph()
G.add_edges_from(ed_ls)
nxpos = nx.spring_layout(G)
nxpts = [nxpos[pt] for pt in sorted(nxpos)]
# nx.draw(G, with_labels=True, pos=nxpos)
# plt.show()

raw_lines = [(pos[x],pos[y]) for x, y in ed_ls]
nx_lines = []
for x, y in ed_ls:
    p1 = nxpos[x].tolist() + [0] # add z-coord
    p2 = nxpos[y].tolist() + [0]
    nx_lines.append([p1,p2])

from vedo import *
raw_pts = Points(pos, r=12)
raw_edg = Lines(raw_lines).lw(2)
show(raw_pts, raw_edg, raw_pts.labels('id'),
     at=0, N=2, axes=True, sharecam=False)

nx_pts = Points(nxpts, r=12)
nx_edg = Lines(nx_lines).lw(2)
show(nx_pts, nx_edg, nx_pts.labels('id'),
     at=1, interactive=True)

write(nx_edg, 'afile.vtk') # save the lines

该包还支持 DirectedGraphs，因此第二个选项是：

from vedo import *
from vedo.pyplot import DirectedGraph

# Layouts: [2d, fast2d, clustering2d, circular, circular3d, cone, force, tree]
g = DirectedGraph(layout='fast2d')
g.arrowScale =0.1
for i in range(6): g.addChild(i)
g.build()
show(g, axes=1)

write(g.unpack(0), 'afile.vtk')

编辑：跟进请求，

如何使用cellColors() 包含基于标量的线条颜色映射：

# ... from the first example

from vedo import *
raw_pts = Points(pos, r=12)
raw_edg = Lines(raw_lines).lw(3)

nx_pts = Points(nxpts, r=12).c('red').alpha(0.5)
nx_edg = Lines(nx_lines).lw(2)

v1 = [sin(x)  for x in range(6)]
v2 = [sqrt(x) for x in range(6)]
vc = [x for x in range(nx_edg.NCells())]
labs1 = nx_pts.labels(v1, scale=.05).c('green').addPos(0.02,.04,0)
labs2 = nx_pts.labels(v2, scale=.05).c('red').addPos(0.02,-.04,0)
labsc = nx_edg.labels(vc, cells=True, scale=.04, precision=1, rotZ=-45)
labsc.c('black')

nx_edg.cellColors(vc, cmap='viridis').addScalarBar3D(c='k').addPos(.2,0,0)
# nx_edg.cellColors(vc, cmap='jet').addScalarBar() # this is a 2D scalarbar

show(nx_pts, nx_edg, labs1, labs2, labsc, axes=1)

如何用鼠标悬停点以弹出带有flag()的标志消息：

from vedo import *
raw_pts = Points(pos, r=12)
raw_edg = Lines(raw_lines).lw(3)

nx_pts = []
for p in nxpts:
    ap = Point(p, r=20).c('red').alpha(0.5)
    ap.flag('some text:\n'+'x='+precision(p[0],2)+'\ny='+precision(p[1],2))
    nx_pts.append(ap)

nx_edg = Lines(nx_lines).lw(3)
show(nx_pts, nx_edg, axes=1)

如何将线条颜色插入节点值：

（注意：这里clean() 删除了重复点，所以请仔细检查可能与初始数组不匹配的点）

from vedo import *

nx_pts = Points(nxpts, r=12).c('grey').alpha(0.5)
nx_edg = Lines(nx_lines).lw(5)

v1 = [sin(x) for x in range(6)]
labs1 = nx_pts.labels(v1, scale=.05).c('green').addPos(0.02,.04,0)

nx_edg.clean().pointColors(v1, cmap='viridis').addScalarBar()

show(nx_pts, nx_edg, labs1, axes=1)