Network_attack_propagation/network_attack_propagation.py

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import random
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib import animation
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class Node:
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def __init__(self, is_infected=False):
self.id = random.randint(1, 2000000)
self.is_infected = is_infected
def as_tuple(self):
return self.id, self.is_infected
def __repr__(self):
return f'id: {self.id}, infected: {self.is_infected}'
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class Edge:
def __init__(self, node_a: Node, node_b: Node, weight: float):
self.node_a = node_a
self.node_b = node_b
self.weight = weight
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def as_tuple(self):
return self.node_a, self.node_b, {'weight': self.weight}
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class Graph:
def __init__(self):
self.edges = []
def add_edge(self, edge: Edge):
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self.edges.append(edge)
def add_edges(self, edges: [Edge]):
[self.edges.append(e) for e in edges]
def update(num, layout, g_repr, ax, our_graph):
"""
This function is called every 'step', so if you wish to update the graph, do it here
"""
ax.clear()
nx.draw_networkx(g_repr, ax=ax, pos=layout)
def do_graph_animation(output_file_name: str, in_graph: Graph, frame_count: int):
g_repr = nx.Graph()
# Convert our graph class into tuples understood by networkx
g_repr.add_edges_from([e.as_tuple() for e in in_graph.edges])
layout = nx.spring_layout(g_repr)
fig, ax = plt.subplots()
anim = animation.FuncAnimation(fig, update, frames=frame_count, fargs=(layout, g_repr, ax, in_graph))
anim.save(output_file_name)
plt.show()
def main():
network = Graph()
nodes = [Node(True), Node(), Node(), Node(True), Node()]
network.add_edges([
Edge(nodes[1], nodes[0], 0.02),
Edge(nodes[1], nodes[2], 0.2),
Edge(nodes[2], nodes[0], 0.7),
Edge(nodes[3], nodes[2], 0.2),
Edge(nodes[3], nodes[1], 0.2),
Edge(nodes[4], nodes[3], 0.2)
])
do_graph_animation('test.gif', network, 1)
if __name__ == "__main__":
main()