mirror of
https://github.com/davidalves04/Trabalho-Pratico-SD.git
synced 2025-12-08 12:33:31 +00:00
Leandro Afonso
a2f9e725de
- Added BatchAnalysisDialog for running multiple simulations and generating reports. - Implemented LeastCongestedRouteSelector for dynamic routing based on congestion levels. - Created RandomRouteSelector for baseline random routing strategy. - Developed ShortestPathRouteSelector to select routes based on the shortest path. - Defined RouteSelector interface to standardize routing policy implementations. - Introduced RoutingPolicy enum to manage available routing strategies.
169 lines
6.5 KiB
Python
169 lines
6.5 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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import glob
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import os
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# Find CSV files using glob
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def load_latest_csv(pattern):
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"""Load the most recent CSV file matching the pattern"""
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files = glob.glob(pattern)
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if not files:
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print(f"Warning: No files found matching '{pattern}'")
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return None
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# Sort by modification time, get the latest
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latest_file = max(files, key=os.path.getmtime)
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print(f"Loading: {latest_file}")
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return pd.read_csv(latest_file)
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# Carregar dados
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print("Looking for analysis files...")
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low = load_latest_csv('analysis/LOW_LOAD_*.csv')
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medium = load_latest_csv('analysis/MEDIUM_LOAD_*.csv')
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high = load_latest_csv('analysis/HIGH_LOAD_*.csv')
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# Check if we have all data
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if low is None or medium is None or high is None:
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print("\nError: Missing analysis files!")
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print("Please run the batch analysis first:")
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exit(1)
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# Print available columns for debugging
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print("\nAvailable columns in LOW_LOAD CSV:")
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print(low.columns.tolist())
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# Create output directory for graphs
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os.makedirs('graphs', exist_ok=True)
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# 1. Gráfico: Dwelling Time vs Load
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plt.figure(figsize=(10, 6))
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dwelling_times = [
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low['TempoMédioSistema'].mean(),
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medium['TempoMédioSistema'].mean(),
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high['TempoMédioSistema'].mean()
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]
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plt.bar(['Low', 'Medium', 'High'], dwelling_times, color=['green', 'orange', 'red'])
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plt.ylabel('Average Dwelling Time (s)')
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plt.title('System Performance vs Load')
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plt.xlabel('Load Scenario')
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plt.grid(axis='y', alpha=0.3)
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for i, v in enumerate(dwelling_times):
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plt.text(i, v + 1, f'{v:.2f}s', ha='center', va='bottom')
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plt.savefig('graphs/dwelling_time_comparison.png', dpi=300, bbox_inches='tight')
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print("\nGraph saved: graphs/dwelling_time_comparison.png")
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plt.close()
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# 2. Gráfico: Completion Rate vs Load
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plt.figure(figsize=(10, 6))
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completion_rates = [
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low['TaxaConclusão'].mean(),
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medium['TaxaConclusão'].mean(),
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high['TaxaConclusão'].mean()
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]
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plt.bar(['Low', 'Medium', 'High'], completion_rates, color=['green', 'orange', 'red'])
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plt.ylabel('Completion Rate (%)')
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plt.title('Vehicle Completion Rate vs Load')
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plt.xlabel('Load Scenario')
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plt.grid(axis='y', alpha=0.3)
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plt.ylim(0, 100)
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for i, v in enumerate(completion_rates):
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plt.text(i, v + 2, f'{v:.1f}%', ha='center', va='bottom')
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plt.savefig('graphs/completion_rate_comparison.png', dpi=300, bbox_inches='tight')
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print("Graph saved: graphs/completion_rate_comparison.png")
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plt.close()
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# 3. Gráfico: Waiting Time vs Load
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plt.figure(figsize=(10, 6))
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waiting_times = [
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low['TempoMédioEspera'].mean(),
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medium['TempoMédioEspera'].mean(),
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high['TempoMédioEspera'].mean()
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]
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plt.bar(['Low', 'Medium', 'High'], waiting_times, color=['green', 'orange', 'red'])
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plt.ylabel('Average Waiting Time (s)')
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plt.title('Average Waiting Time vs Load')
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plt.xlabel('Load Scenario')
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plt.grid(axis='y', alpha=0.3)
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for i, v in enumerate(waiting_times):
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plt.text(i, v + 1, f'{v:.2f}s', ha='center', va='bottom')
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plt.savefig('graphs/waiting_time_comparison.png', dpi=300, bbox_inches='tight')
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print("Graph saved: graphs/waiting_time_comparison.png")
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plt.close()
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# 4. Gráfico: Summary Statistics
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(14, 10))
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loads = ['Low', 'Medium', 'High']
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# Vehicles generated
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ax1.bar(loads, [low['VeículosGerados'].mean(), medium['VeículosGerados'].mean(), high['VeículosGerados'].mean()], color=['green', 'orange', 'red'])
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ax1.set_title('Vehicles Generated')
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ax1.set_ylabel('Count')
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ax1.grid(axis='y', alpha=0.3)
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# Vehicles completed
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ax2.bar(loads, [low['VeículosCompletados'].mean(), medium['VeículosCompletados'].mean(), high['VeículosCompletados'].mean()], color=['green', 'orange', 'red'])
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ax2.set_title('Vehicles Completed')
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ax2.set_ylabel('Count')
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ax2.grid(axis='y', alpha=0.3)
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# Min/Max dwelling time
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x = range(3)
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width = 0.35
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ax3.bar([i - width/2 for i in x], [low['TempoMínimoSistema'].mean(), medium['TempoMínimoSistema'].mean(), high['TempoMínimoSistema'].mean()], width, label='Min', color='lightblue')
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ax3.bar([i + width/2 for i in x], [low['TempoMáximoSistema'].mean(), medium['TempoMáximoSistema'].mean(), high['TempoMáximoSistema'].mean()], width, label='Max', color='darkblue')
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ax3.set_title('Min/Max Dwelling Time')
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ax3.set_ylabel('Time (s)')
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ax3.set_xticks(x)
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ax3.set_xticklabels(loads)
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ax3.legend()
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ax3.grid(axis='y', alpha=0.3)
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# Performance summary
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metrics = ['Dwelling\nTime', 'Waiting\nTime', 'Completion\nRate']
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low_vals = [low['TempoMédioSistema'].mean(), low['TempoMédioEspera'].mean(), low['TaxaConclusão'].mean()]
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med_vals = [medium['TempoMédioSistema'].mean(), medium['TempoMédioEspera'].mean(), medium['TaxaConclusão'].mean()]
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high_vals = [high['TempoMédioSistema'].mean(), high['TempoMédioEspera'].mean(), high['TaxaConclusão'].mean()]
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x = range(len(metrics))
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width = 0.25
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ax4.bar([i - width for i in x], low_vals, width, label='Low', color='green')
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ax4.bar(x, med_vals, width, label='Medium', color='orange')
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ax4.bar([i + width for i in x], high_vals, width, label='High', color='red')
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ax4.set_title('Performance Summary')
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ax4.set_xticks(x)
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ax4.set_xticklabels(metrics)
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ax4.legend()
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ax4.grid(axis='y', alpha=0.3)
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plt.tight_layout()
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plt.savefig('graphs/summary_statistics.png', dpi=300, bbox_inches='tight')
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print("Graph saved: graphs/summary_statistics.png")
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plt.close()
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# Print summary statistics
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print("\n" + "="*60)
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print("SUMMARY STATISTICS")
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print("="*60)
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print(f"\nLOW LOAD:")
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print(f" Avg Dwelling Time: {low['TempoMédioSistema'].mean():.2f}s")
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print(f" Avg Waiting Time: {low['TempoMédioEspera'].mean():.2f}s")
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print(f" Completion Rate: {low['TaxaConclusão'].mean():.1f}%")
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print(f" Vehicles Generated: {low['VeículosGerados'].mean():.0f}")
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print(f" Vehicles Completed: {low['VeículosCompletados'].mean():.0f}")
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print(f"\nMEDIUM LOAD:")
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print(f" Avg Dwelling Time: {medium['TempoMédioSistema'].mean():.2f}s")
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print(f" Avg Waiting Time: {medium['TempoMédioEspera'].mean():.2f}s")
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print(f" Completion Rate: {medium['TaxaConclusão'].mean():.1f}%")
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print(f" Vehicles Generated: {medium['VeículosGerados'].mean():.0f}")
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print(f" Vehicles Completed: {medium['VeículosCompletados'].mean():.0f}")
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print(f"\nHIGH LOAD:")
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print(f" Avg Dwelling Time: {high['TempoMédioSistema'].mean():.2f}s")
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print(f" Avg Waiting Time: {high['TempoMédioEspera'].mean():.2f}s")
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print(f" Completion Rate: {high['TaxaConclusão'].mean():.1f}%")
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print(f" Vehicles Generated: {high['VeículosGerados'].mean():.0f}")
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print(f" Vehicles Completed: {high['VeículosCompletados'].mean():.0f}")
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print("\n" + "="*60)
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print("All graphs saved in 'graphs/' directory!")
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print("="*60) |