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https://github.com/logos-blockchain/logos-blockchain-specs.git
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55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
import argparse
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn
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from config import Config
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from simulation import Simulation
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run simulation", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("--config", type=str, required=True, help="Configuration file path")
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args = parser.parse_args()
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config = Config.load(args.config)
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sim = Simulation(config)
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sim.run()
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# Stat the distribution of message sizes
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df = pd.DataFrame(sim.p2p.message_sizes, columns=["message_size"])
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print(df.describe())
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# Visualize the nodes emitted messages around the promised interval
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df = pd.DataFrame(
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[(node.id, cnt, node.id < len(config.real_message_prob_weights))
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for node, cnt in sim.p2p.senders_around_interval.items()],
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columns=["NodeID", "MsgCount", "Expected"]
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)
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plt.figure(figsize=(10, 6))
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seaborn.barplot(data=df, x="NodeID", y="MsgCount", 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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# Analyze the number of mixed messages per node per observation window
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dataframes = []
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for mixed_msgs_per_node in sim.p2p.mixed_msgs_per_window:
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df = pd.DataFrame([(node.id, cnt) for node, cnt in mixed_msgs_per_node.items()], columns=["NodeID", "MsgCount"])
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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="NodeID", values="MsgCount")
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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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print("Simulation complete!") |