使用mayavi绘制三维网络时删除节点

Question

我使用Networkx创建了一个图形网络，并使用Mayavi绘制。

创建图形后，我将删除具有度的节点

import matplotlib.pyplot as plt
from mayavi import mlab
import numpy as np
import pandas as pd
    
    
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)
remove = [node for node, degree in dict(G.degree()).items() if degree < 2]
G.remove_nodes_from(remove)
pos.drop(pos.index[remove], inplace=True)

print(G.edges)
    
nx.draw(G)
plt.show()
    
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()  # interactive window

我想询问有关如何删除删除的节点和相应位置并在输出中显示其余节点的建议。

其次，我想知道如何以交互方式删除节点和连接到这些节点的边。例如，如果要删除连接到度节点的节点和边

编辑：我试图从数据帧pos中删除已删除节点的位置，方法是在上面发布的完整代码中包含pos.drop（pos.index[删除]， inplace=True）更新。

但我仍然没有得到正确的输出。

Answer 1

这是一个在Mayavi中交互式删除网络节点和边缘的解决方案（我认为matplotlib可能足够简单，但无论如何……）。

该解决方案的灵感来自此Mayavi示例。但是，由于图示符（用于显示节点）由多个点组成，因此该示例不能直接传递，并且在单独绘制每个图示符/节点时，不能使用点id来识别图示符/节点。此外，它不包括隐藏/删除对象的选项。为了避免这些问题，我使用了四个想法：

>

每个节点/边缘都被绘制为一个单独的对象，因此更容易调整它的（可见性）属性。

单击节点/边时，它们将隐藏，而不是删除节点/边。此外，单击两次会使节点再次可见（这对下面代码的边不起作用，但如果需要，您可能可以实现这一点，只需要跟踪可见节点）。可以在末尾收集可见节点（请参见下面的代码）。

如示例中所示，使用选择器回调捕获鼠标位置。但不是使用最近点的点id，而是直接使用其坐标。

通过计算鼠标位置与所有节点之间的最小欧氏距离，找到要删除/隐藏的节点。

PS：在您的原始代码中，forhtml" target="_blank">循环是非常冗余的，因为它多次将所有节点和边绘制在彼此的顶部。

希望这有帮助！

# import modules
from mayavi import mlab
import numpy as np
import pandas as pd
import networkx as nx

# set number of nodes
number = 6

# create random node positions
np.random.seed(5)
pos = 100*np.random.rand(6, 3)
pos = pd.DataFrame(pos, columns=['x', 'y', 'z'])

# create chain graph links
links = [(x, y) for x, y in zip(range(0, number-1), range(1, number))]

# create graph (not strictly needed, link list above would be enough)
graph = nx.Graph()
graph.add_edges_from(links)

# setup mayavi figure
figure = mlab.gcf()
mlab.clf()

# add nodes as individual glyphs
# store glyphs in dictionary to allow interactive adjustments of visibility
color = (0.5, 0.0, 0.5)
nodes = dict()
texts = dict()
for ni, n in enumerate(graph.nodes()):
    xyz = pos.loc[n]
    n = mlab.points3d(xyz['x'], xyz['y'], xyz['z'], scale_factor=5, color=color)
    label = 'node %s' % ni
    t = mlab.text3d(xyz['x'], xyz['y'], xyz['z']+5, label, scale=(5, 5, 5))
    # each glyph consists of many points
    # arr = n.glyph.glyph_source.glyph_source.output.points.to_array()
    nodes[ni] = n
    texts[ni] = t

# add edges as individual tubes
edges = dict()
for ei, e in enumerate(graph.edges()):
    xyz = pos.loc[np.array(e)]
    edges[ei] = mlab.plot3d(xyz['x'], xyz['y'], xyz['z'], tube_radius=1, color=color)


# define picker callback for figure interaction
def picker_callback(picker):
    # get coordinates of mouse click position
    cen = picker.pick_position
    # compute Euclidean distance btween mouse position and all nodes
    dist = np.linalg.norm(pos-cen, axis=1)
    # get closest node
    ni = np.argmin(dist)
    # hide/show node and text
    n = nodes[ni]
    n.visible = not n.visible
    t = texts[ni]
    t.visible = not t.visible
    # hide/show edges
    # must be adjusted if double-clicking should hide/show both nodes and edges in a reasonable way
    for ei, edge in enumerate(graph.edges()):
        if ni in edge:
            e = edges[ei]
            e.visible = not e.visible


# add picker callback
picker = figure.on_mouse_pick(picker_callback)
picker.tolerance = 0.01

# show interactive window
# mlab.show()

# collect visibility/deletion status of nodes, e.g.
# [(0, True), (1, False), (2, True), (3, True), (4, True), (5, True)]
[(key, node.visible) for key, node in nodes.items()]