使用 matplotlib 在网络中设置动态节点形状

Question

第一次发帖，请轻点。 :)

我正在尝试在 Networkx 中绘制不同类型的字符网络，并希望为每种类型设置不同的节点形状。例如，我希望角色是圆形，生物是三角形等。我已经尝试了几个小时并进行了广泛的搜索，但除了为每种类型的字符设置不同的 node_lists 并分别渲染它们，这似乎违反直觉。

问题是我无法从内部访问 node_shape 字典值：

nx.draw_networkx_nodes(G, pos) 

我尝试了多种解决方案，包括尝试访问节点属性、创建外部字典或列表并从调用中访问它、设置列表理解或迭代器，但似乎没有任何效果。

要么我传递一个批量提取的列表，一个函数无法散列的字典，要么传递一个列表的实例，例如shape_list.pop(0)，在这种情况下，函数只接受第一个值并将其应用于所有节点。

我可以通过创建一个单独的 node_colors 列表来设置颜色，该列表由函数迭代，甚至尝试创建一个字典，以便 node_shape 由 node_color 触发，但这也不起作用。

我希望将代码用作使用 Python 3.4 和 Django 1.8 开发的网络应用程序的附加组件，因此 Graphviz 不是一个选项。

提前感谢您对备用库的任何帮助或参考。

这是我的代码：

import json
import requests
import networkx as nx
import matplotlib.pyplot as plt

personas = 'http://story-chronicles.herokuapp.com/storyobjects/'
target = requests.get(personas)
x = target.json()

story_objects = {}
labels = {}
node_colors = []

for character in x:
    name = character["name"]
    story = character["story"]
    c_type = character["c_type"]
    story_objects[name] = {}
    story_objects[name]['name'] = name
    story_objects[name]['story'] = story
    story_objects[name]['c_type'] = c_type
    story_objects[name]['to_relationships'] = []
    if character['c_type'] == "Character":
        story_objects[name]['node_shape'] = 'o'
        story_objects[name]['node_color'] = 'r'
    elif character['c_type'] == "Organization":
        story_objects[name]['node_shape'] = 'h'
        story_objects[name]['node_color'] = 'b'
    elif character['c_type'] == "Creature":
        story_objects[name]['node_shape'] = '^'
        story_objects[name]['node_color'] = 'g'
    elif character['c_type'] == "Force":
        story_objects[name]['node_shape'] = 'v'
        story_objects[name]['node_color'] = 'c'
    elif character['c_type'] == "Thing":
        story_objects[name]['node_shape'] = 's'
        story_objects[name]['node_color'] = 'y'

    for relationship in character["to_relationships"]:
        break_1 = relationship.find(">>")
        break_2 = relationship.find("weight:")
        sub_1 = relationship[0:break_1].strip()
        context = relationship[break_1:break_2]
        weight = relationship[break_2+8:-1]
        story_objects[name]['to_relationships'].append([sub_1, context, weight])

G=nx.MultiDiGraph()

for sub in story_objects:
    s = story_objects[sub]
    if s['story'] == "http://story-chronicles.herokuapp.com/story/1/":
        G.add_node(s['name'], node_shape=s['node_shape'])
        labels[s['name']] = s['name']

        node_colors.append(s['node_color'])

        print("***", s['name'], "***", s['c_type'])
        print("details:", s['node_color'], s['node_shape'])
        for i in s['to_relationships']:
            print('target:', i[0])
            print('context:', i[1])
            print('weight:', i[2])
            G.add_edge(s['name'], i[0], weight=int(i[2]))
        print("")

node_shapes=nx.get_node_attributes(G, 'node_shape') # Latest attempt at getting this to work
node_shapes = [v for k,v in node_shapes.items()]

pos=nx.spring_layout(G)
G.degree(weight=weight)

nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_shape=node_shapes.pop(0)) # <--- This is where I'm having problems
nx.draw_networkx_edges(G, pos)
nx.draw_networkx_labels(G, pos, labels)

plt.show()

Answer 1

恐怕这必须通过多次传递来完成。

主要思想是使用layout获取节点的位置，然后对n不同类别的节点重复使用draw_networkx_nodes。

例如：

import networkx
import pylab

#Build a graph (Node attribute 's' determines the node shape here)
G = networkx.Graph()
G.add_node(0, s="^", b=1)
G.add_node(1, s="^", b=2)

G.add_node(2, s="o", b=3)
G.add_node(3, s="o", b=4)

G.add_node(4, s="v", b=5)
G.add_node(5, s="v", b=6)

networkx.add_path(G, [0,2,5])
networkx.add_path(G, [1,4,3,0])
networkx.add_path(G, [2,4,0,5])

#Drawing the graph
#First obtain the node positions using one of the layouts
nodePos = networkx.layout.spring_layout(G)

#The rest of the code here attempts to automate the whole process by
#first determining how many different node classes (according to
#attribute 's') exist in the node set and then repeatedly calling 
#draw_networkx_node for each. Perhaps this part can be optimised further.

#Get all distinct node classes according to the node shape attribute
nodeShapes = set((aShape[1]["s"] for aShape in G.nodes(data = True)))

#For each node class...
for aShape in nodeShapes:
    #...filter and draw the subset of nodes with the same symbol in the positions that are now known through the use of the layout.
    networkx.draw_networkx_nodes(G,nodePos,node_shape = aShape, nodelist = [sNode[0] for sNode in filter(lambda x: x[1]["s"]==aShape,G.nodes(data = True))])

#Finally, draw the edges between the nodes
networkx.draw_networkx_edges(G,nodePos)

#And show the final result
pylab.show()

最终结果如下所示：

希望这会有所帮助。

Answer 2

这里是较早的帖子（A_A）的轻微变化。我的使用几乎相同的东西，只放一条画线并包含一些标签，并显示一个二分图。它可能对互联网上的其他人有用，所以我把它放在这里。enter image description here

import networkx as nx
% matplotlib inline
from networkx.algorithms import bipartite
B = nx.Graph()
B.add_nodes_from(['$x_1$','$x_2$','$x_3$'], s='o', bipartite=0) # Add the node attribute 'bipartite'
B.add_nodes_from(['$f_a$','$f_b$','$f_c$','$f_d$'], s='s', bipartite=1)
B.add_edges_from([('$x_1$','$f_a$'),('$x_1$','$f_b$'),('$x_2$','$f_a$'),('$x_2$','$f_b$'),('$x_2$','$f_c$'),('$x_3$','$f_c$'),('$x_3$','$f_d$')])

pos = dict()
X, Y = bipartite.sets(B)
pos.update((n, (i,1)) for i, n in enumerate(X))
pos.update((n, (i+0.5,2)) for i, n in enumerate(Y))
nodeShapes = set((aShape[1]["s"] for aShape in B.nodes(data=True)))

for aShape in nodeShapes:
    nx.draw(
        B, 
        pos, 
        with_labels=True, 
        node_shape = aShape, 
        node_color = '0.95', 
        node_size=1000, 
        nodelist = [
            sNode[0] for sNode in filter(lambda x: x[1]["s"]==aShape, B.nodes(data=True))
        ]
    )

plt.savefig("img/15_Graphical_Models_12b.png") # save as png