TraceRT命令可视化实验-Windows版

原创于 2025-02-27 12:06:27 发布 · 671 阅读

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#windows

善用大模型，效果图：

第一步，在cmd窗口中运行traceRT命令

如下：

C:\Users\Thinkpad>traceRT baidu.com

通过最多 30 个跃点跟踪

到 baidu.com [110.242.68.66] 的路由:

1 * * * 请求超时。

2 44 ms 45 ms 44 ms 172.17.0.42

3 * 44 ms 44 ms 172.17.0.253

4 43 ms 43 ms 46 ms 61.50.130.93

5 74 ms 40 ms 45 ms 123.126.6.53

6 * * * 请求超时。

7 * * * 请求超时。

8 * * * 请求超时。

9 53 ms * 53 ms 110.242.66.166

10 53 ms 54 ms 53 ms 221.194.45.130

11 * * * 请求超时。

12 * * * 请求超时。

13 * * * 请求超时。

14 * * * 请求超时。

15 54 ms 51 ms 50 ms 110.242.68.66

跟踪完成。

C:\Users\Thinkpad>traceRT bing.com

通过最多 30 个跃点跟踪

到 bing.com [204.79.197.200] 的路由:

1 5 ms * 3 ms 10.115.255.254

2 4 ms 2 ms * 172.17.0.42

3 2 ms 2 ms 2 ms 172.17.0.253

4 12 ms 20 ms 6 ms 61.50.130.93

5 5 ms 6 ms 3 ms 123.126.6.53

6 * 5 ms * 221.216.106.101

7 * * * 请求超时。

8 * * * 请求超时。

9 * 40 ms * 219.158.3.158

10 45 ms 41 ms 47 ms 219.158.4.2

11 80 ms * 80 ms 219.158.12.218

12 80 ms 81 ms 73 ms 219.158.38.218

13 * * * 请求超时。

14 82 ms 75 ms 80 ms 13.104.185.160

15 * * * 请求超时。

16 * * * 请求超时。

17 71 ms 72 ms 70 ms a-0001.a-msedge.net [204.79.197.200]

跟踪完成。

第二步，用python绘图

!终端中的bash命令安装第三方库matplotlib和network

pip install matplotlib networkx

分别运行两段代码

import matplotlib.pyplot as plt
import networkx as nx

# Creating a directed graph
G = nx.DiGraph()

# Adding nodes and edges based on the traceRT data
nodes = ["Start", "172.17.0.42", "172.17.0.253", "61.50.130.93", "123.126.6.53", "Timeout", "Timeout", "Timeout",
         "110.242.66.166", "221.194.45.130", "Timeout", "Timeout", "Timeout", "Timeout", "110.242.68.66"]
edges = [(nodes[i], nodes[i+1]) for i in range(len(nodes)-1) if "Timeout" not in nodes[i]]

# Adding nodes and edges to the graph
G.add_nodes_from(nodes)
G.add_edges_from(edges)

# Drawing the graph
pos = nx.spring_layout(G)
plt.figure(figsize=(12, 8))

# Drawing nodes
nx.draw_networkx_nodes(G, pos, node_size=2000)
nx.draw_networkx_nodes(G, pos, nodelist=[node for node in nodes if "Timeout" in node], node_color='red', node_size=2000)

# Drawing edges
nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)
nx.draw_networkx_edges(G, pos, edgelist=[edge for edge in edges if "Timeout" not in edge[1]], width=2.0, alpha=0.5)

# Drawing node labels
nx.draw_networkx_labels(G, pos, font_size=10)

# Showing the plot
plt.title("Network Topology Diagram based on traceRT to baidu.com")
plt.show()

import matplotlib.pyplot as plt
import networkx as nx

# Data for the network topology
nodes = [
    "10.115.255.254", "172.17.0.42", "172.17.0.253", "61.50.130.93",
    "123.126.6.53", "221.216.106.101", "Timeout", "Timeout",
    "219.158.3.158", "219.158.4.2", "219.158.12.218", "219.158.38.218",
    "Timeout", "13.104.185.160", "Timeout", "Timeout",
    "a-0001.a-msedge.net [204.79.197.200]"
]

edges = [(nodes[i], nodes[i+1]) for i in range(len(nodes)-1)]

# Create graph
G = nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)

# Draw the graph
plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_color="skyblue", node_size=2000, font_size=10, font_weight="bold")
nx.draw_networkx_nodes(G, pos, nodelist=["Timeout", "Timeout", "Timeout", "Timeout", "Timeout"], node_color="red")

# Show the plot
plt.title("Network Topology to bing.com [204.79.197.200]")
plt.show()