250 lines
7.1 KiB
Python
250 lines
7.1 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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self.under_attack = False
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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 clear_attacked(self) -> None:
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for edge in self.edges:
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edge.node_a.under_attack = False
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edge.node_b.under_attack = False
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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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to_be_infected.under_attack = 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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edgecolors = ['black' if n.under_attack else 'none' for n in g_repr]
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linewidths = [3 if c == 'black' else 0 for c in edgecolors]
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sizes = [300 if n.is_infected else 150 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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linewidths=linewidths,
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edgecolors=edgecolors,
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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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our_graph.clear_attacked()
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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 degree_avg(edges, digits=2):
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degrees = {}
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for e in edges:
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degrees[e.node_a] = degrees.get(e.node_a, 0) + 1
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degrees[e.node_b] = degrees.get(e.node_b, 0) + 1
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return round(mean(degrees.values()), digits)
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def bus_network(n=30, infected_idx=0) -> tuple[Graph, float, int]:
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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, degree_avg(edges), n
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def star_network(cluster_count=5, starsize=6) -> tuple[Graph, float, int]:
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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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nodes[starsize-1].is_infected = True
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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, degree_avg(edges), node_count
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def ring_network(n=30) -> tuple[Graph, float, int]:
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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, degree_avg(edges), n
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def summary(average_degrees: list[float], propagation_speeds: list[float]) -> None:
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fig, ax = plt.subplots()
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ax.plot(average_degrees, propagation_speeds)
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ax.set(xlabel='Average degree', ylabel='Propagation speed', title='Summary')
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fig.savefig("summary.png")
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plt.show()
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def experiment(network, avg_degree, node_count, iteration):
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degrees = []
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speeds = []
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do_graph_animation(f'bus{iteration}.gif', network, 90, nx.spiral_layout)
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speeds.append(node_count / network.rounds_survived)
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degrees.append(avg_degree)
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print(f"\n{node_count} NODE BUS")
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print(f"average degree = {avg_degree}")
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print(f"propagation speed = {round(speeds[-1], 2)}")
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print(f"bus_{iteration} rounds survived = {network.rounds_survived + 1}")
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summary(degrees, speeds)
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def bus_experiment():
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sizes = [0, 8, 15]
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for i in sizes:
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bus, bus_avg_degree, node_count = bus_network(20 + i)
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experiment(bus, bus_avg_degree, node_count, i)
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def ring_experiment():
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sizes = [0, 8, 15]
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for i in sizes:
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ring, ring_avg_degree, node_count = ring_network(20 + i)
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experiment(ring, ring_avg_degree, node_count, i)
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def star_experiment():
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sizes = [0, 8, 15]
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for i in sizes:
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star, star_avg_degree, node_count = star_network(cluster_count=3 + i, starsize=3 + i)
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experiment(star, star_avg_degree, node_count, i)
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def weighted_star_experiment():
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def main():
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bus_experiment()
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ring_experiment()
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star_experiment()
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if __name__ == "__main__":
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main()
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