200 lines
5.5 KiB
Python
200 lines
5.5 KiB
Python
import random
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from statistics import mean
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import matplotlib.pyplot as plt
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import networkx as nx
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from matplotlib import animation
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class Node:
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def __init__(self, is_infected=False):
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self.id = random.randint(1, 2000000)
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self.is_infected = is_infected
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def as_tuple(self):
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return self.id, self.is_infected
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def __repr__(self):
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return f'id: {self.id}, infected: {self.is_infected}'
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class Edge:
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def __init__(self, node_a: Node, node_b: Node, weight: float):
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self.node_a = node_a
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self.node_b = node_b
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self.weight = weight
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def as_tuple(self):
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return self.node_a, self.node_b, {'weight': self.weight}
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def has_node(self, node: Node) -> bool:
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return self.node_a is node or self.node_b is node
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class Graph:
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def __init__(self, use_weights=False):
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self.edges = []
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self.use_weights = use_weights
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self.rounds_survived = 0
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def add_edge(self, edge: Edge):
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self.edges.append(edge)
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def add_edges(self, edges: [Edge]):
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[self.edges.append(e) for e in edges]
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def get_nodes(self) -> [Node]:
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nodes = set()
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for edge in self.edges:
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nodes.add(edge.node_a)
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nodes.add(edge.node_b)
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return nodes
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def get_adjacent_nodes(self, node: Node) -> [(Node, int)]:
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"""
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:param node: Node to search for
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:return: An array of tuples (node, weight)
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"""
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edges_with_node = filter(lambda ed: ed.has_node(node), self.edges)
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nodes = set()
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for e in edges_with_node:
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if e.node_a is node:
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nodes.add((e.node_b, e.weight))
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else:
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nodes.add((e.node_a, e.weight))
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return nodes
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def is_alive(self):
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nodes_alive = list(filter(lambda x: not x.is_infected, self.get_nodes()))
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return len(nodes_alive) > 0
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def update_survived(self):
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if not self.is_alive():
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return
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self.rounds_survived += 1
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def infect_step(self):
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infected_nodes = list(filter(lambda n: n.is_infected, self.get_nodes()))
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for node in infected_nodes:
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adjacent_nodes = self.get_adjacent_nodes(node)
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if self.use_weights:
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to_be_infected = random.choices([n[0] for n in adjacent_nodes], weights=[n[1] for n in adjacent_nodes])[0]
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else:
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to_be_infected = random.choice([n[0] for n in adjacent_nodes])
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to_be_infected.is_infected = True
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self.update_survived()
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def update(num, layout, g_repr, ax, our_graph: Graph):
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"""
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This function is called every 'step', so if you wish to update the graph, do it here
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"""
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if not our_graph.is_alive():
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return
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if num != 0:
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our_graph.infect_step()
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ax.clear()
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ax.set_title(f'Step: {num}', loc='right', fontsize=30)
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colors = ['red' if n.is_infected else 'blue' for n in g_repr]
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sizes = [200 if n.is_infected else 50 for n in g_repr]
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nx.draw(
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g_repr,
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ax=ax,
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pos=layout,
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node_color=colors,
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with_labels=False,
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node_size=sizes,
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alpha=0.7,
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)
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def do_graph_animation(output_file_name: str, in_graph: Graph, frame_count: int, layout):
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g_repr = nx.Graph()
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# Convert our graph class into tuples understood by networkx
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g_repr.add_edges_from([e.as_tuple() for e in in_graph.edges])
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layout = layout(g_repr)
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fig, ax = plt.subplots()
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fig.set_figwidth(8)
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fig.set_figheight(8)
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anim = animation.FuncAnimation(
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fig, update, frames=frame_count, interval=500, fargs=(layout, g_repr, ax, in_graph)
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)
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anim.save(output_file_name)
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plt.style.use('seaborn')
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plt.show()
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def bus_network(n=30, infected_idx=0) -> Graph:
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network = Graph()
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nodes = [Node() for _ in range(n)]
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nodes[infected_idx].is_infected = True
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edges = [Edge(nodes[i], nodes[i + 1], 1.0) for i in range(n - 1)]
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network.add_edges(edges)
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return network
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def rank_avg(edges, digits=2):
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ranks = {}
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for e in edges:
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ranks[e.node_a] = ranks.get(e.node_a, 0) + 1
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ranks[e.node_b] = ranks.get(e.node_b, 0) + 1
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return round(mean(ranks.values()), digits)
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def star_network(cluster_count=5, starsize=6) -> tuple[Graph, float]:
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node_count = cluster_count + cluster_count * starsize + 1
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nodes = [Node() for _ in range(node_count)]
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edges = []
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for x in range(cluster_count):
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center_node = x * starsize + x
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edges += [Edge(nodes[center_node], nodes[i], 1.0) for i in range(center_node + 1, center_node + starsize + 1)]
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edges.append(Edge(nodes[-1], nodes[center_node], 1.0))
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network = Graph()
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network.add_edges(edges)
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return network, rank_avg(edges)
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def ring_network(n=30) -> Graph:
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network = Graph()
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nodes = [Node() for _ in range(n)]
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nodes[0].is_infected = True
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edges = [Edge(nodes[i], nodes[i + 1], 1.0) for i in range(n - 1)]
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end_edge = Edge(nodes[n - 1], nodes[0], 1.0)
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edges.append(end_edge)
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network.add_edges(edges)
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return network
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def main():
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bus = bus_network(36)
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do_graph_animation('bus.gif', bus, 90, nx.spiral_layout)
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ring = ring_network(36)
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do_graph_animation('ring.gif', ring, 90, nx.circular_layout)
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star, star_avg_rank = star_network()
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list(star.get_nodes())[1].is_infected = True
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do_graph_animation('star.gif', star, 90, nx.kamada_kawai_layout)
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print(f'bus survived {bus.rounds_survived} rounds')
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print(f'ring survived {ring.rounds_survived} rounds')
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print(f'star survived {star.rounds_survived} rounds')
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if __name__ == "__main__":
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main()
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