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72 lines
1.7 KiB
Python
72 lines
1.7 KiB
Python
#!/usr/bin/env python3
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# coding: utf-8
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import networkx as nx
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import matplotlib.pyplot as plt
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import sys
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import ujson as json
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get_ipython().run_line_magic('matplotlib', 'inline')
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filename = 'lngraph0721'
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# Load channel graph exported from LND
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# To create the JSON file, use the following command
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# on an LND node that is fully synced:
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# lndcli describegraph > filename.json
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describegraph = open(filename+'.json', 'r')
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lngraph = json.load(describegraph)
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# Construct network graph
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graph = nx.Graph()
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# Add edges and nodes
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for edge in lngraph['edges']:
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# Name the nodes by last 4 characters of node ID
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node1 = edge['node1_pub'][-4:]
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node2 = edge['node2_pub'][-4:]
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graph.add_node(node1)
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graph.add_node(node2)
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graph.add_edge(node1, node2)
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# Show graph info before reduction
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print(nx.info(graph))
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# Remove nodes with fewer than 3 channels to make graph cleaner
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remove = [node for node,degree in dict(graph.degree()).items() if degree < 3]
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graph.remove_nodes_from(remove)
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# Show graph info after reduction
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print(nx.info(graph))
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# Set figure/diagram options (thin grey lines for channels, black dots for nodes)
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options = {
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"node_color": "black",
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"node_size": 2,
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"edge_color" : "grey",
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"linewidths": 0,
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"width": 0.01,
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}
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# 16:9 image ratio
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fig = plt.figure(figsize=(16,9))
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# Spring layout arranges nodes automatically.
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# Channels cause "attraction" (spring), nodes cause "repulsion" (opposite)
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# k controls distance between nodes. Spread them out to make graph less dense
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# Seed for reproducible layout
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pos = nx.spring_layout(graph,k=0.4, iterations=10, seed=721)
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nx.draw(graph, pos, **options)
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# Save PNG image
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plt.savefig(filename+'.png', format="png", dpi=600)
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