我需要绘制一个在两个节点之间具有多个边（具有不同权重）的有向图。也就是说，我有节点 A 和 B 以及长度 = 2 的边 (A,B) 和长度 = 3 的边 (B,A)。
我尝试过使用 G=nx.Digraph 和 G=nx.Multidigraph。当我绘制它时，我只能看到一条边缘和一个标签。 有什么办法可以做到吗？
Here is how to get an outcome similar to the following:
它的一些属性是：
以下几行是启动示例的初始代码
import matplotlib.pyplot as plt
import networkx as nx
G = nx.DiGraph()
edge_list = [(1,2,{'w':'A1'}),(2,1,{'w':'A2'}),(2,3,{'w':'B'}),(3,1,{'w':'C'}),
(3,4,{'w':'D1'}),(4,3,{'w':'D2'}),(1,5,{'w':'E1'}),(5,1,{'w':'E2'}),
(3,5,{'w':'F'}),(5,4,{'w':'G'})]
G.add_edges_from(edge_list)
pos=nx.spring_layout(G,seed=5)
fig, ax = plt.subplots()
nx.draw_networkx_nodes(G, pos, ax=ax)
nx.draw_networkx_labels(G, pos, ax=ax)
fig.savefig("1.png", bbox_inches='tight',pad_inches=0)
结果是：
The draw_networkx_edgesNetworkX 的函数只能绘制边的子集edgelist范围。为了使用它，我们将边缘分组到两个列表中并分别绘制它们。谢谢阿曼吉平托的回答 https://stackoverflow.com/a/59917339 for connectionstyle='arc3, rad = 0.1'.
curved_edges = [edge for edge in G.edges() if reversed(edge) in G.edges()]
straight_edges = list(set(G.edges()) - set(curved_edges))
nx.draw_networkx_edges(G, pos, ax=ax, edgelist=straight_edges)
arc_rad = 0.25
nx.draw_networkx_edges(G, pos, ax=ax, edgelist=curved_edges, connectionstyle=f'arc3, rad = {arc_rad}')
fig.savefig("2.png", bbox_inches='tight',pad_inches=0)
结果是：
The draw_networkx_edge_labelsNetworkX 的函数假设边缘是直的，并且没有参数可以改变这一点。由于 NetworkX 是开源的，我复制了该函数并创建了一个修改后的my_draw_networkx_edge_labels。这个函数在附录中。
假设您将此函数保存到名为 my_networkx.py 的文件中，您可以将边缘标签绘制为：
import my_networkx as my_nx
edge_weights = nx.get_edge_attributes(G,'w')
curved_edge_labels = {edge: edge_weights[edge] for edge in curved_edges}
straight_edge_labels = {edge: edge_weights[edge] for edge in straight_edges}
my_nx.my_draw_networkx_edge_labels(G, pos, ax=ax, edge_labels=curved_edge_labels,rotate=False,rad = arc_rad)
nx.draw_networkx_edge_labels(G, pos, ax=ax, edge_labels=straight_edge_labels,rotate=False)
fig.savefig("3.png", bbox_inches='tight',pad_inches=0)
我们再次将曲线与直线分开。结果是这个答案中的第一个数字。
功能draw_networkx_edge_labelsNetworkX 找到假设直线的标签位置：
(x, y) = (
x1 * label_pos + x2 * (1.0 - label_pos),
y1 * label_pos + y2 * (1.0 - label_pos),
)
要找到二次贝塞尔曲线的中点，我们可以使用以下代码。首先我们找到中间控制点（ctrl_1根据定义的贝塞尔曲线绘图库 https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.ConnectionStyle.html#matplotlib.patches.ConnectionStyle.Arc3:
创建曲线以便中间控制点 (C1) 位于 距起点 (C0) 和终点 (C2) 的距离相同 C1 到连接 C0-C2 的线的距离是 rad 乘以 C0-C2的距离。
由于这个定义，函数my_draw_networkx_edge_labels需要一个额外的参数称为rad.
pos_1 = ax.transData.transform(np.array(pos[n1]))
pos_2 = ax.transData.transform(np.array(pos[n2]))
linear_mid = 0.5*pos_1 + 0.5*pos_2
d_pos = pos_2 - pos_1
rotation_matrix = np.array([(0,1), (-1,0)])
ctrl_1 = linear_mid + rad*rotation_matrix@d_pos
以“ax.transData”开头的函数是必需的，因为轴域中的 90 度角与显示中的 90 度不对应。因此我们必须在显示坐标系之间进行坐标转换。
bezier_mid可以用贝塞尔曲线规则计算：
ctrl_mid_1 = 0.5*pos_1 + 0.5*ctrl_1
ctrl_mid_2 = 0.5*pos_2 + 0.5*ctrl_1
bezier_mid = 0.5*ctrl_mid_1 + 0.5*ctrl_mid_2
(x, y) = ax.transData.inverted().transform(bezier_mid)
完全的my_draw_networkx_edge_labels:
def my_draw_networkx_edge_labels(
G,
pos,
edge_labels=None,
label_pos=0.5,
font_size=10,
font_color="k",
font_family="sans-serif",
font_weight="normal",
alpha=None,
bbox=None,
horizontalalignment="center",
verticalalignment="center",
ax=None,
rotate=True,
clip_on=True,
rad=0
):
"""Draw edge labels.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edge_labels : dictionary (default={})
Edge labels in a dictionary of labels keyed by edge two-tuple.
Only labels for the keys in the dictionary are drawn.
label_pos : float (default=0.5)
Position of edge label along edge (0=head, 0.5=center, 1=tail)
font_size : int (default=10)
Font size for text labels
font_color : string (default='k' black)
Font color string
font_weight : string (default='normal')
Font weight
font_family : string (default='sans-serif')
Font family
alpha : float or None (default=None)
The text transparency
bbox : Matplotlib bbox, optional
Specify text box properties (e.g. shape, color etc.) for edge labels.
Default is {boxstyle='round', ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)}.
horizontalalignment : string (default='center')
Horizontal alignment {'center', 'right', 'left'}
verticalalignment : string (default='center')
Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
rotate : bool (deafult=True)
Rotate edge labels to lie parallel to edges
clip_on : bool (default=True)
Turn on clipping of edge labels at axis boundaries
Returns
-------
dict
`dict` of labels keyed by edge
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.org/documentation/latest/auto_examples/index.html
See Also
--------
draw
draw_networkx
draw_networkx_nodes
draw_networkx_edges
draw_networkx_labels
"""
import matplotlib.pyplot as plt
import numpy as np
if ax is None:
ax = plt.gca()
if edge_labels is None:
labels = {(u, v): d for u, v, d in G.edges(data=True)}
else:
labels = edge_labels
text_items = {}
for (n1, n2), label in labels.items():
(x1, y1) = pos[n1]
(x2, y2) = pos[n2]
(x, y) = (
x1 * label_pos + x2 * (1.0 - label_pos),
y1 * label_pos + y2 * (1.0 - label_pos),
)
pos_1 = ax.transData.transform(np.array(pos[n1]))
pos_2 = ax.transData.transform(np.array(pos[n2]))
linear_mid = 0.5*pos_1 + 0.5*pos_2
d_pos = pos_2 - pos_1
rotation_matrix = np.array([(0,1), (-1,0)])
ctrl_1 = linear_mid + rad*rotation_matrix@d_pos
ctrl_mid_1 = 0.5*pos_1 + 0.5*ctrl_1
ctrl_mid_2 = 0.5*pos_2 + 0.5*ctrl_1
bezier_mid = 0.5*ctrl_mid_1 + 0.5*ctrl_mid_2
(x, y) = ax.transData.inverted().transform(bezier_mid)
if rotate:
# in degrees
angle = np.arctan2(y2 - y1, x2 - x1) / (2.0 * np.pi) * 360
# make label orientation "right-side-up"
if angle > 90:
angle -= 180
if angle < -90:
angle += 180
# transform data coordinate angle to screen coordinate angle
xy = np.array((x, y))
trans_angle = ax.transData.transform_angles(
np.array((angle,)), xy.reshape((1, 2))
)[0]
else:
trans_angle = 0.0
# use default box of white with white border
if bbox is None:
bbox = dict(boxstyle="round", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0))
if not isinstance(label, str):
label = str(label) # this makes "1" and 1 labeled the same
t = ax.text(
x,
y,
label,
size=font_size,
color=font_color,
family=font_family,
weight=font_weight,
alpha=alpha,
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
rotation=trans_angle,
transform=ax.transData,
bbox=bbox,
zorder=1,
clip_on=clip_on,
)
text_items[(n1, n2)] = t
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return text_items