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https://github.com/logos-blockchain/logos-blockchain-specs.git
synced 2026-01-08 08:03:13 +00:00
71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
import pandas as pd
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import seaborn
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from matplotlib import pyplot as plt
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from adversary import NodeState
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from simulation import Simulation
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class Analysis:
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def __init__(self, sim: Simulation):
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self.sim = sim
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def run(self):
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self.message_size_distribution()
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self.messages_emitted_around_interval()
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self.mixed_messages_per_node_over_time()
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self.node_states()
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def message_size_distribution(self):
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df = pd.DataFrame(self.sim.p2p.adversary.message_sizes, columns=["message_size"])
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print(df.describe())
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def messages_emitted_around_interval(self):
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df = pd.DataFrame(
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[(node.id, cnt, node.id < len(self.sim.config.mixnet.real_message_prob_weights))
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for node, cnt in self.sim.p2p.adversary.senders_around_interval.items()],
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columns=["node_id", "msg_count", "expected"]
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)
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plt.figure(figsize=(10, 6))
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seaborn.barplot(data=df, x="node_id", y="msg_count", hue="expected", palette={True: "red", False: "blue"})
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plt.title("Messages emitted around the promised interval")
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plt.xlabel("Sender Node ID")
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plt.ylabel("Msg Count")
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plt.legend(title="expected")
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plt.show()
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def mixed_messages_per_node_over_time(self):
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dataframes = []
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for mixed_msgs_per_node in self.sim.p2p.adversary.mixed_msgs_per_window:
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df = pd.DataFrame([(node.id, cnt) for node, cnt in mixed_msgs_per_node.items()],
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columns=["node_id", "msg_count"])
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dataframes.append(df)
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observation_times = range(len(dataframes))
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df = pd.concat([df.assign(Time=time) for df, time in zip(dataframes, observation_times)], ignore_index=True)
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df = df.pivot(index="Time", columns="node_id", values="msg_count")
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plt.figure(figsize=(12, 6))
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for column in df.columns:
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plt.plot(df.index, df[column], marker="o", label=column)
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plt.title("Mixed messages in each mix over time")
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plt.xlabel("Time")
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plt.ylabel("Msg Count")
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plt.legend(title="Node ID")
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plt.grid(True)
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plt.show()
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def node_states(self):
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rows = []
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for time, node_states in self.sim.p2p.adversary.node_states.items():
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for node, state in node_states.items():
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rows.append((time, node.id, state))
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df = pd.DataFrame(rows, columns=["time", "node_id", "state"])
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plt.figure(figsize=(10, 6))
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seaborn.scatterplot(data=df, x="time", y="node_id", hue="state", palette={NodeState.SENDING: "red", NodeState.RECEIVING: "blue"})
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plt.title("Node states over time")
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plt.xlabel("Time")
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plt.ylabel("Node ID")
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plt.legend(title="state")
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plt.show()
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