networkx - 根据边缘属性更改颜色/宽度 - 结果不一致

Question

我设法正确地生成了图表，但经过更多测试发现以下两行不同代码的结果不一致：

colors = [h.edge[i][j]['color'] for (i,j) in h.edges_iter()]
widths = [h.edge[i][j]['width'] for (i,j) in h.edges_iter()]
nx.draw_circular(h, edge_color=colors, width=widths)

这种方法会产生一致的输出，而以下方法会根据边缘的顺序产生错误的颜色/大小：

colors = list(nx.get_edge_attributes(h,'color').values())
widths = list(nx.get_edge_attributes(h,'width').values())
nx.draw_circular(h, edge_color=colors, width=widths)

但是，在我看来，以上两行都依赖于函数调用来按边的顺序返回属性。为什么会有不同的结果？

用h[][][] 访问属性对我来说有点笨拙；是否可以通过点约定访问它，例如edge.color for edge in h.edges().

还是我错过了什么？

Answer 1

如果您想避免手动添加边缘颜色和 alphas / 宽度，您可能还会发现此功能很有帮助：

def rgb_to_hex(rgb):
    return '#%02x%02x%02x' % rgb

adjacency_matrix = np.array([[0, 0, 0.5], [1, 0, 1], [1, 0.5, 0]]))
n_graphs = 5
fig, axs = plt.subplots(1, len(n_graphs), figsize=(19,2.5)) 

for graph in range(n_graphs):   

    pos = {0: (1, 0.9), 1: (0.9, 1), 2: (1.1, 1)} 

    # draw DAG graph from adjacency matrix 
    gr = nx.from_numpy_matrix(adjacency_matrix, create_using=nx.DiGraph)
    weights = nx.get_edge_attributes(gr, "weight")
  
    # adding nodes 
    all_rows = range(0, adjacency_matrix.shape[0])
    for n in all_rows:
        gr.add_node(n)
    
    # getting edges 
    edges = gr.edges()
      
    # weight and color of edges 
    scaling_factor = 4 # to emphasise differences 
    alphas = [weights[edge] * scaling_factor for edge in edges]
    colors = [rgb_to_hex(tuple(np.repeat(int(255 * (1- 
    weights[edge])),3))) for edge in edges]
    
    # draw graph 
    nx.draw(gr, 
            pos, 
            ax=axs[graph],
            edgecolors='black', 
            node_color='white', 
            node_size=2000, 
            labels={0: "A", 1: "B", 2: "C"},
            font_weight='bold',
            linewidths=2,
            with_labels=True,
            connectionstyle="arc3,rad=0.15",
            edge_color=colors,
            width=alphas)

  
plt.tight_layout()

Answer 2

字典是用于 NetworkX 图的底层数据结构，从 Python 3.7+ 开始，它们是 maintain insertion order。 这意味着我们可以安全地使用nx.get_edge_attributes 来检索边缘属性，因为我们保证在每次运行Graph.edges()（内部由get_edge_attributes 调用）中具有相同的边缘顺序。

所以在绘图的时候，我们可以直接从get_edge_attributes返回的结果中设置edge_color和width等属性。这是一个例子：

G = nx.Graph()
G.add_edge(0,1,color='r',weight=2)
G.add_edge(1,2,color='g',weight=4)
G.add_edge(2,3,color='b',weight=6)
G.add_edge(3,4,color='y',weight=3)
G.add_edge(4,0,color='m',weight=1)

colors = nx.get_edge_attributes(G,'color').values()
weights = nx.get_edge_attributes(G,'weight').values()

pos = nx.circular_layout(G)
nx.draw(G, pos, 
        edge_color=colors, 
        width=list(weights),
        with_labels=True,
        node_color='lightgreen')

Answer 3

传递给绘图函数的边的顺序很重要。如果您不指定（使用 edges 关键字），您将获得 G.edges() 的默认顺序。像这样显式地给出参数是最安全的：

import networkx as nx

G = nx.Graph()
G.add_edge(1,2,color='r',weight=2)
G.add_edge(2,3,color='b',weight=4)
G.add_edge(3,4,color='g',weight=6)

pos = nx.circular_layout(G)

edges = G.edges()
colors = [G[u][v]['color'] for u,v in edges]
weights = [G[u][v]['weight'] for u,v in edges]

nx.draw(G, pos, edges=edges, edge_color=colors, width=weights)

这会产生如下输出：