# 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()