nomos-specs/mixnet/v2/sim/analysis.py

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import pandas as pd
import seaborn
from matplotlib import pyplot as plt
from simulation import Simulation
class Analysis:
def __init__(self, sim: Simulation):
self.sim = sim
def run(self):
self.message_size_distribution()
self.messages_emitted_around_interval()
self.mixed_messages_per_node_over_time()
def message_size_distribution(self):
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df = pd.DataFrame(self.sim.p2p.adversary.message_sizes, columns=["message_size"])
print(df.describe())
def messages_emitted_around_interval(self):
df = pd.DataFrame(
[(node.id, cnt, node.id < len(self.sim.config.real_message_prob_weights))
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for node, cnt in self.sim.p2p.adversary.senders_around_interval.items()],
columns=["node_id", "msg_count", "expected"]
)
plt.figure(figsize=(10, 6))
seaborn.barplot(data=df, x="node_id", y="msg_count", hue="expected", palette={True: "red", False: "blue"})
plt.title("Messages emitted around the promised interval")
plt.xlabel("Sender Node ID")
plt.ylabel("Msg Count")
plt.legend(title="expected")
plt.show()
def mixed_messages_per_node_over_time(self):
dataframes = []
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for mixed_msgs_per_node in self.sim.p2p.adversary.mixed_msgs_per_window:
df = pd.DataFrame([(node.id, cnt) for node, cnt in mixed_msgs_per_node.items()],
columns=["node_id", "msg_count"])
dataframes.append(df)
observation_times = range(len(dataframes))
df = pd.concat([df.assign(Time=time) for df, time in zip(dataframes, observation_times)], ignore_index=True)
df = df.pivot(index="Time", columns="node_id", values="msg_count")
plt.figure(figsize=(12, 6))
for column in df.columns:
plt.plot(df.index, df[column], marker="o", label=column)
plt.title("Mixed messages in each mix over time")
plt.xlabel("Time")
plt.ylabel("Msg Count")
plt.legend(title="Node ID")
plt.grid(True)
plt.show()