diff --git a/main/graphing.py b/main/graphing.py index 8980165..9bda368 100644 --- a/main/graphing.py +++ b/main/graphing.py @@ -41,10 +41,10 @@ dwelling_times = [ medium['TempoMédioSistema'].mean(), high['TempoMédioSistema'].mean() ] -plt.bar(['Low', 'Medium', 'High'], dwelling_times, color=['green', 'orange', 'red']) -plt.ylabel('Average Dwelling Time (s)') -plt.title('System Performance vs Load') -plt.xlabel('Load Scenario') +plt.bar(['Baixa', 'Média', 'Alta'], dwelling_times, color=['green', 'orange', 'red']) +plt.ylabel('Tempo Médio no Sistema (s)') +plt.title('Desempenho do Sistema vs Carga') +plt.xlabel('Cenário de Carga') plt.grid(axis='y', alpha=0.3) for i, v in enumerate(dwelling_times): plt.text(i, v + 1, f'{v:.2f}s', ha='center', va='bottom') @@ -59,10 +59,10 @@ completion_rates = [ medium['TaxaConclusão'].mean(), high['TaxaConclusão'].mean() ] -plt.bar(['Low', 'Medium', 'High'], completion_rates, color=['green', 'orange', 'red']) -plt.ylabel('Completion Rate (%)') -plt.title('Vehicle Completion Rate vs Load') -plt.xlabel('Load Scenario') +plt.bar(['Baixa', 'Média', 'Alta'], completion_rates, color=['green', 'orange', 'red']) +plt.ylabel('Taxa de Conclusão (%)') +plt.title('Taxa de Conclusão de Veículos vs Carga') +plt.xlabel('Cenário de Carga') plt.grid(axis='y', alpha=0.3) plt.ylim(0, 100) for i, v in enumerate(completion_rates): @@ -78,10 +78,10 @@ waiting_times = [ medium['TempoMédioEspera'].mean(), high['TempoMédioEspera'].mean() ] -plt.bar(['Low', 'Medium', 'High'], waiting_times, color=['green', 'orange', 'red']) -plt.ylabel('Average Waiting Time (s)') -plt.title('Average Waiting Time vs Load') -plt.xlabel('Load Scenario') +plt.bar(['Baixa', 'Média', 'Alta'], waiting_times, color=['green', 'orange', 'red']) +plt.ylabel('Tempo Médio de Espera (s)') +plt.title('Tempo Médio de Espera vs Carga') +plt.xlabel('Cenário de Carga') plt.grid(axis='y', alpha=0.3) for i, v in enumerate(waiting_times): plt.text(i, v + 1, f'{v:.2f}s', ha='center', va='bottom') @@ -91,44 +91,44 @@ plt.close() # 4. Gráfico: Summary Statistics fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(14, 10)) -loads = ['Low', 'Medium', 'High'] +loads = ['Baixa', 'Média', 'Alta'] # Vehicles generated ax1.bar(loads, [low['VeículosGerados'].mean(), medium['VeículosGerados'].mean(), high['VeículosGerados'].mean()], color=['green', 'orange', 'red']) -ax1.set_title('Vehicles Generated') -ax1.set_ylabel('Count') +ax1.set_title('Veículos Gerados') +ax1.set_ylabel('Quantidade') ax1.grid(axis='y', alpha=0.3) # Vehicles completed ax2.bar(loads, [low['VeículosCompletados'].mean(), medium['VeículosCompletados'].mean(), high['VeículosCompletados'].mean()], color=['green', 'orange', 'red']) -ax2.set_title('Vehicles Completed') -ax2.set_ylabel('Count') +ax2.set_title('Veículos Concluídos') +ax2.set_ylabel('Quantidade') ax2.grid(axis='y', alpha=0.3) # Min/Max dwelling time x = range(3) width = 0.35 -ax3.bar([i - width/2 for i in x], [low['TempoMínimoSistema'].mean(), medium['TempoMínimoSistema'].mean(), high['TempoMínimoSistema'].mean()], width, label='Min', color='lightblue') -ax3.bar([i + width/2 for i in x], [low['TempoMáximoSistema'].mean(), medium['TempoMáximoSistema'].mean(), high['TempoMáximoSistema'].mean()], width, label='Max', color='darkblue') -ax3.set_title('Min/Max Dwelling Time') -ax3.set_ylabel('Time (s)') +ax3.bar([i - width/2 for i in x], [low['TempoMínimoSistema'].mean(), medium['TempoMínimoSistema'].mean(), high['TempoMínimoSistema'].mean()], width, label='Mín', color='lightblue') +ax3.bar([i + width/2 for i in x], [low['TempoMáximoSistema'].mean(), medium['TempoMáximoSistema'].mean(), high['TempoMáximoSistema'].mean()], width, label='Máx', color='darkblue') +ax3.set_title('Tempo no Sistema Mín/Máx') +ax3.set_ylabel('Tempo (s)') ax3.set_xticks(x) ax3.set_xticklabels(loads) ax3.legend() ax3.grid(axis='y', alpha=0.3) # Performance summary -metrics = ['Dwelling\nTime', 'Waiting\nTime', 'Completion\nRate'] +metrics = ['Tempo no\nSistema', 'Tempo de\nEspera', 'Taxa de\nConclusão'] low_vals = [low['TempoMédioSistema'].mean(), low['TempoMédioEspera'].mean(), low['TaxaConclusão'].mean()] med_vals = [medium['TempoMédioSistema'].mean(), medium['TempoMédioEspera'].mean(), medium['TaxaConclusão'].mean()] high_vals = [high['TempoMédioSistema'].mean(), high['TempoMédioEspera'].mean(), high['TaxaConclusão'].mean()] x = range(len(metrics)) width = 0.25 -ax4.bar([i - width for i in x], low_vals, width, label='Low', color='green') -ax4.bar(x, med_vals, width, label='Medium', color='orange') -ax4.bar([i + width for i in x], high_vals, width, label='High', color='red') -ax4.set_title('Performance Summary') +ax4.bar([i - width for i in x], low_vals, width, label='Baixa', color='green') +ax4.bar(x, med_vals, width, label='Média', color='orange') +ax4.bar([i + width for i in x], high_vals, width, label='Alta', color='red') +ax4.set_title('Resumo de Desempenho') ax4.set_xticks(x) ax4.set_xticklabels(metrics) ax4.legend() diff --git a/main/graphs/completion_rate_comparison.png b/main/graphs/completion_rate_comparison.png index aa7071f..a86957b 100644 Binary files a/main/graphs/completion_rate_comparison.png and b/main/graphs/completion_rate_comparison.png differ diff --git a/main/graphs/dwelling_time_comparison.png b/main/graphs/dwelling_time_comparison.png index 60bc18e..d56ec41 100644 Binary files a/main/graphs/dwelling_time_comparison.png and b/main/graphs/dwelling_time_comparison.png differ diff --git a/main/graphs/summary_statistics.png b/main/graphs/summary_statistics.png index 481282e..c3dd6c4 100644 Binary files a/main/graphs/summary_statistics.png and b/main/graphs/summary_statistics.png differ diff --git a/main/graphs/waiting_time_comparison.png b/main/graphs/waiting_time_comparison.png index 5d400b8..8d03cc8 100644 Binary files a/main/graphs/waiting_time_comparison.png and b/main/graphs/waiting_time_comparison.png differ