我正在使用 Plotly 的 Python 界面生成网络。我设法用我想要的节点和边创建了一个网络，并控制了节点的大小。 我正在绝望地寻求有关如何执行以下操作的帮助:
- 添加节点标签
- 根据权重列表添加边缘标签
- 根据权重列表控制边缘线宽
所有这一切都没有使用“悬停”选项，因为它必须放在非交互式纸张中。我将不胜感激任何帮助! Plotly's output | In case this fails, the figure itself | matrix.csv 这是我的代码(大部分是从 Networkx 的 Plotly 教程中复制粘贴的):
import pandas as pd
import plotly.plotly as py
from plotly.graph_objs import *
import networkx as nx
matrix = pd.read_csv("matrix.csv", sep = "\t", index_col = 0, header = 0)
G = nx.DiGraph()
# add nodes:
G.add_nodes_from(matrix.columns)
# add edges:
edge_lst = [(i,j, matrix.loc[i,j])
for i in matrix.index
for j in matrix.columns
if matrix.loc[i,j] != 0]
G.add_weighted_edges_from(edge_lst)
# create node trace:
node_trace = Scatter(x = [], y = [], text = [], mode = 'markers',
marker = Marker(
showscale = True,
colorscale = 'YIGnBu',
reversescale = True,
color = [],
size = [],
colorbar = dict(
thickness = 15,
title = 'Node Connections',
xanchor = 'left',
titleside = 'right'),
line = dict(width = 2)))
# set node positions
pos = nx.spring_layout(G)
for node in G.nodes():
G.node[node]['pos']= pos[node]
for node in G.nodes():
x, y = G.node[node]['pos']
node_trace['x'].append(x)
node_trace['y'].append(y)
# create edge trace:
edge_trace = Scatter(x = [], y = [], text = [],
line = Line(width = [], color = '#888'),
mode = 'lines')
for edge in G.edges():
x0, y0 = G.node[edge[0]]['pos']
x1, y1 = G.node[edge[1]]['pos']
edge_trace['x'] += [x0, x1, None]
edge_trace['y'] += [y0, y1, None]
edge_trace['text'] += str(matrix.loc[edge[0], edge[1]])[:5]
# size nodes by degree
deg_dict = {deg[0]:int(deg[1]) for deg in list(G.degree())}
for node, degree in enumerate(deg_dict):
node_trace['marker']['size'].append(deg_dict[degree] + 20)
fig = Figure(data = Data([edge_trace, node_trace]),
layout = Layout(
title = '<br>AA Substitution Rates',
titlefont = dict(size = 16),
showlegend = True,
margin = dict(b = 20, l = 5, r = 5, t = 40),
annotations = [dict(
text = "sub title text",
showarrow = False,
xref = "paper", yref = "paper",
x = 0.005, y = -0.002)],
xaxis = XAxis(showgrid = False,
zeroline = False,
showticklabels = False),
yaxis = YAxis(showgrid = False,
zeroline = False,
showticklabels = False)))
py.plot(fig, filename = 'networkx')
最佳答案
所以
1. 这个问题的解决方案相对简单，您可以创建一个包含节点 ID 的列表，并将其设置在散点图的 text 属性中。然后将模式设置为“标记+文本”，就完成了。
2. 这有点棘手。您必须计算每行的中间位置并创建一个字典列表，包括该行的中间位置和粗细。然后添加 set 作为布局的注释。
3. 这太复杂了，无法使用 plotly IMO 来完成。至于现在，我正在使用 networkx spring_layout 函数计算每个节点的位置。如果您想根据每条线的重量设置每条线的宽度，则必须使用一个函数修改位置，该函数考虑到每条线所附的所有标记。
奖励我为您提供了以不同方式为图表的每个组件着色的选项。
这是我刚才做的一个(稍微修改过的)函数，它执行 1 和 2:
import pandas as pd
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import networkx as nx
def scatter_plot_2d(G, folderPath, name, savePng = False):
print("Creating scatter plot (2D)...")
Nodes = [comp for comp in nx.connected_components(G)] # Looks for the graph's communities
Edges = G.edges()
edge_weights = nx.get_edge_attributes(G,'weight')
labels = [] # names of the nodes to plot
group = [] # id of the communities
group_cnt = 0
print("Communities | Number of Nodes")
for subgroup in Nodes:
group_cnt += 1
print(" %d | %d" % (group_cnt, len(subgroup)))
for node in subgroup:
labels.append(int(node))
group.append(group_cnt)
labels, group = (list(t) for t in zip(*sorted(zip(labels, group))))
layt = nx.spring_layout(G, dim=2) # Generates the layout of the graph
Xn = [layt[k][0] for k in list(layt.keys())] # x-coordinates of nodes
Yn = [layt[k][1] for k in list(layt.keys())] # y-coordinates
Xe = []
Ye = []
plot_weights = []
for e in Edges:
Xe += [layt[e[0]][0], layt[e[1]][0], None]
Ye += [layt[e[0]][1], layt[e[1]][1], None]
ax = (layt[e[0]][0]+layt[e[1]][0])/2
ay = (layt[e[0]][1]+layt[e[1]][1])/2
plot_weights.append((edge_weights[(e[0], e[1])], ax, ay))
annotations_list =[
dict(
x=plot_weight[1],
y=plot_weight[2],
xref='x',
yref='y',
text=plot_weight[0],
showarrow=True,
arrowhead=7,
ax=plot_weight[1],
ay=plot_weight[2]
)
for plot_weight in plot_weights
]
trace1 = go.Scatter( x=Xe,
y=Ye,
mode='lines',
line=dict(color='rgb(90, 90, 90)', width=1),
hoverinfo='none'
)
trace2 = go.Scatter( x=Xn,
y=Yn,
mode='markers+text',
name='Nodes',
marker=dict(symbol='circle',
size=8,
color=group,
colorscale='Viridis',
line=dict(color='rgb(255,255,255)', width=1)
),
text=labels,
textposition='top center',
hoverinfo='none'
)
xaxis = dict(
backgroundcolor="rgb(200, 200, 230)",
gridcolor="rgb(255, 255, 255)",
showbackground=True,
zerolinecolor="rgb(255, 255, 255)"
)
yaxis = dict(
backgroundcolor="rgb(230, 200,230)",
gridcolor="rgb(255, 255, 255)",
showbackground=True,
zerolinecolor="rgb(255, 255, 255)"
)
layout = go.Layout(
title=name,
width=700,
height=700,
showlegend=False,
plot_bgcolor="rgb(230, 230, 200)",
scene=dict(
xaxis=dict(xaxis),
yaxis=dict(yaxis)
),
margin=dict(
t=100
),
hovermode='closest',
annotations=annotations_list
, )
data = [trace1, trace2]
fig = go.Figure(data=data, layout=layout)
plotDir = folderPath + "/"
print("Plotting..")
if savePng:
plot(fig, filename=plotDir + name + ".html", auto_open=True, image = 'png', image_filename=plotDir + name,
output_type='file', image_width=700, image_height=700, validate=False)
else:
plot(fig, filename=plotDir + name + ".html")
关于python - 使用 Python Plotly 自定义 Networkx 图(或散点图)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/50078361/