script_plot_mestrado.py

import pandas as pd
import matplotlib.pyplot as plt
import os 
import numpy as np
# Leitura do arquivo

main_folder = "./"

scenarios = [name for name in os.listdir(main_folder) if os.path.isdir(os.path.join(main_folder, name))]

scenarios = sorted(
    scenarios,
    key=lambda x: (
        x != "no_slicing",  # Garante que "no_slicing" venha no final
        int(x.split('_')[-1]) if x.startswith('scen_') and x.split('_')[-1].isdigit() else float('inf')
    )
)

ue_dataframes = {}

for scenario in scenarios:
    ue_dataframes[scenario] = {}
    scenario_path = main_folder + scenario
    cap_confs = [name for name in os.listdir(scenario_path) if os.path.isdir(os.path.join(scenario_path, name))] ## Configuração da capacidade dos slices
    for cap_conf in cap_confs: 
        ue_dataframes[scenario][cap_conf] = {}
        cap_path = scenario_path + '/' + cap_conf
        ues = [name for name in os.listdir(cap_path) if os.path.isdir(os.path.join(cap_path, name))]
        for ue in ues: 
            data = []
            ue_path = cap_path + '/' + ue + '/' + 'job_1.txt'
            ##Organização do dataframe

            with open(ue_path, 'r') as file:
                count = 0
                data.append({
                                "Interval": 0,
                                "Transfer": 0,
                                "Bandwidth": 0,
                                "Jitter": 0,
                                "PacketsLost": 0,
                                "TotalPackets": 0
                            })
                for line in file:
                    if "[  3]" in line and "sec" in line and "MBytes" in line:
                        parts = line.split()
                        count += 1
                        print(parts)
                        if count < 10: 
                            data.append({
                                "Interval": count,
                                "Transfer": parts[5],
                                "Bandwidth": float(parts[7]),
                                "Jitter": parts[9] if len(parts) > 6 else None,
                                "PacketsLost": int(parts[-2].split("/")[0]) if "/" in parts[-2] else int(parts[-3].split("/")[0]),
                                "TotalPackets": int(parts[-2].split("/")[1]) if "/" in parts[-2] else int(parts[-2])
                            })
                        elif count < 60:
                            data.append({
                                "Interval": count,
                                "Transfer": parts[4],
                                "Bandwidth": float(parts[6]),
                                "Jitter": parts[8] if len(parts) > 6 else None,
                                "PacketsLost": int(parts[-2].split("/")[0]) if "/" in parts[-2] else int(parts[-3].split("/")[0]),
                                "TotalPackets": int(parts[-2].split("/")[1]) if "/" in parts[-2] else int(parts[-2])
                            })
                        else:
                            break
            ue_dataframes[scenario][cap_conf][ue] = pd.DataFrame(data)
pl_ue1 = []
pl_ue2 = []

for scenario in scenarios:
    scenario_path = main_folder + scenario
    cap_confs = [name for name in os.listdir(scenario_path) if os.path.isdir(os.path.join(scenario_path, name))] ## Configuração da capacidade dos slices
    cap_confs = sorted(cap_confs, key=lambda x: (x == 'no_slicing', x))
    for cap_conf in cap_confs:
        plt.plot(ue_dataframes[scenario][cap_conf]['ue2']["Interval"], ue_dataframes[scenario][cap_conf]['ue2']["Bandwidth"], marker="o", label="UE2", color = "blue")
        plt.plot(ue_dataframes[scenario][cap_conf]['ue1']["Interval"], ue_dataframes[scenario][cap_conf]['ue1']["Bandwidth"], marker="o", label="UE2", color = "red")
        y_ticks = np.arange(0, max(ue_dataframes[scenario][cap_conf]['ue2']["Bandwidth"].max(),
                              ue_dataframes[scenario][cap_conf]['ue1']["Bandwidth"].max()) + 10, 2)  # Intervalo de 2 Mbps
        plt.yticks(y_ticks)  # Define os valores do eixo Y com intervalos mais detalhados  
        plt.title(scenario)
        plt.xlabel("Time (s)")
        plt.ylabel("Bandwidth (Mbps)")
        plt.grid(True)
        plt.legend()
        plt.tight_layout()
        pl_ue1.append((ue_dataframes[scenario][cap_conf]['ue1']["PacketsLost"].sum()/ue_dataframes[scenario][cap_conf]['ue1']["TotalPackets"].sum())*100)
        pl_ue2.append((ue_dataframes[scenario][cap_conf]['ue2']["PacketsLost"].sum()/ue_dataframes[scenario][cap_conf]['ue2']["TotalPackets"].sum())*100)
        plt.show()

    # Configurar a posição das barras no eixo x
    x = np.arange(len(cap_confs))  # Posições das scenarios
    print(x)
    largura = 0.4  # Largura das barras

    # Criar as barras
    plt.bar(x - largura / 2, pl_ue1, width=largura, label='UE1', color='red')  # Barras para UE1
    plt.bar(x + largura / 2, pl_ue2, width=largura, label='UE2', color='blue')  # Barras para UE2

    # Adicionar rótulos e título
    plt.xlabel('Scenarios')
    plt.ylabel('Packet Loss')
    plt.title('Packet Loss for each capacity configuration')
    plt.xticks(x, cap_confs)  # Ajustar rótulos do eixo x
    plt.legend()  # Adicionar legenda

    # Exibir o gráfico
    plt.tight_layout()
    plt.show()