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diff --git a/analysis/e1anl/src/analyze.py b/analysis/e1anl/src/analyze.py
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+++ b/analysis/e1anl/src/analyze.py
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+# analyze_metrics.py
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+import os
+os.makedirs("output", exist_ok=True)
+
+def analyze():
+    CSV_FILE = "output/rtt_throughput_log.csv"
+    df = pd.read_csv(CSV_FILE)
+
+    df['sent_ms'] = df['sent_ms'].astype(int)
+    df['received_ms'] = df['received_ms'].astype(int)
+    df['rtt_ms'] = df['rtt_ms'].astype(int)
+    df['throughput_msg_per_s'] = df['throughput_msg_per_s'].astype(float)
+
+    mean_rtt = df['rtt_ms'].mean()
+    p95_rtt = df['rtt_ms'].quantile(0.95)
+    mean_throughput = df['throughput_msg_per_s'].mean()
+
+    print(f"Średnie RTT: {mean_rtt:.2f} ms")
+    print(f"RTT 95-percentyl: {p95_rtt:.2f} ms")
+    print(f"Średni throughput: {mean_throughput:.2f} msg/s")
+
+    plt.figure(figsize=(6,4))
+    sns.barplot(x=['Variant A'], y=[mean_rtt], palette="Set2")
+    plt.title("Średnie RTT - wariant A")
+    plt.ylabel("RTT [ms]")
+    plt.tight_layout()
+    plt.savefig("output/rtt_mean_a.png")
+    plt.show()
+
+    plt.figure(figsize=(6,4))
+    sns.barplot(x=['Variant A'], y=[p95_rtt], palette="Set3")
+    plt.title("RTT 95-percentyl - wariant A")
+    plt.ylabel("RTT [ms]")
+    plt.tight_layout()
+    plt.savefig("output/rtt_p95_a.png")
+    plt.show()
+
+    plt.figure(figsize=(6,4))
+    sns.barplot(x=['Variant A'], y=[mean_throughput], palette="Set1")
+    plt.title("Średni throughput - wariant A")
+    plt.ylabel("Messages per second")
+    plt.tight_layout()
+    plt.savefig("output/throughput_a.png")
+    plt.show()