mirror of
https://github.com/davidalves04/Trabalho-Pratico-SD.git
synced 2025-12-12 06:23:30 +00:00
218 lines
8.2 KiB
Python
218 lines
8.2 KiB
Python
import pandas as pd
|
|
import matplotlib.pyplot as plt
|
|
import glob
|
|
import os
|
|
import sys
|
|
|
|
# Find CSV files using glob
|
|
def load_latest_csv(pattern):
|
|
"""Load the most recent CSV file matching the pattern"""
|
|
files = glob.glob(pattern)
|
|
if not files:
|
|
print(f"Warning: No files found matching '{pattern}'")
|
|
return None
|
|
# Sort by modification time, get the latest
|
|
latest_file = max(files, key=os.path.getmtime)
|
|
print(f"Loading: {latest_file}")
|
|
return pd.read_csv(latest_file)
|
|
|
|
# Determine which load to analyze
|
|
if len(sys.argv) > 1:
|
|
load_type = sys.argv[1].upper()
|
|
else:
|
|
print("Usage: python graphing_single.py [LOW|MEDIUM|HIGH]")
|
|
print("Defaulting to LOW_LOAD...")
|
|
load_type = "LOW"
|
|
|
|
# Validate load type
|
|
if load_type not in ["LOW", "MEDIUM", "HIGH"]:
|
|
print(f"Error: Invalid load type '{load_type}'. Must be LOW, MEDIUM, or HIGH.")
|
|
exit(1)
|
|
|
|
# Load the specified data
|
|
print(f"Looking for {load_type}_LOAD analysis files...")
|
|
pattern = f'analysis/{load_type}_LOAD_*.csv'
|
|
data = load_latest_csv(pattern)
|
|
|
|
if data is None:
|
|
print(f"\nError: No {load_type}_LOAD analysis files found!")
|
|
print("Please run the batch analysis first.")
|
|
exit(1)
|
|
|
|
# Print available columns for debugging
|
|
print(f"\nAvailable columns in {load_type}_LOAD CSV:")
|
|
print(data.columns.tolist())
|
|
|
|
# Create output directory for graphs
|
|
os.makedirs('graphs', exist_ok=True)
|
|
|
|
# Determine the run column name (could be 'Run' or 'Execução')
|
|
run_col = 'Run' if 'Run' in data.columns else 'Execução'
|
|
|
|
# Load name for titles
|
|
load_name = load_type.capitalize()
|
|
colors = {'LOW': 'green', 'MEDIUM': 'orange', 'HIGH': 'red'}
|
|
color = colors[load_type]
|
|
|
|
# 1. Gráfico: Run-by-Run Dwelling Time
|
|
plt.figure(figsize=(12, 6))
|
|
plt.plot(data[run_col], data['TempoMédioSistema'], marker='o', color=color, linewidth=2, markersize=6)
|
|
plt.fill_between(data[run_col], data['TempoMínimoSistema'], data['TempoMáximoSistema'], alpha=0.2, color=color)
|
|
plt.ylabel('Tempo no Sistema (s)')
|
|
plt.xlabel('Run Number')
|
|
plt.title(f'Tempo no Sistema por Execução - Carga {load_name}')
|
|
plt.grid(alpha=0.3)
|
|
plt.legend(['Média', 'Min-Max Range'])
|
|
plt.savefig(f'graphs/{load_type.lower()}_dwelling_time_runs.png', dpi=300, bbox_inches='tight')
|
|
print(f"\nGraph saved: graphs/{load_type.lower()}_dwelling_time_runs.png")
|
|
plt.close()
|
|
|
|
# 2. Gráfico: Run-by-Run Completion Rate
|
|
plt.figure(figsize=(12, 6))
|
|
plt.plot(data[run_col], data['TaxaConclusão'], marker='o', color=color, linewidth=2, markersize=6)
|
|
plt.axhline(y=data['TaxaConclusão'].mean(), color='black', linestyle='--', label=f'Média: {data["TaxaConclusão"].mean():.1f}%')
|
|
plt.ylabel('Taxa de Conclusão (%)')
|
|
plt.xlabel('Run Number')
|
|
plt.title(f'Taxa de Conclusão por Execução - Carga {load_name}')
|
|
plt.ylim(0, 105)
|
|
plt.grid(alpha=0.3)
|
|
plt.legend()
|
|
plt.savefig(f'graphs/{load_type.lower()}_completion_rate_runs.png', dpi=300, bbox_inches='tight')
|
|
print(f"Graph saved: graphs/{load_type.lower()}_completion_rate_runs.png")
|
|
plt.close()
|
|
|
|
# 3. Gráfico: Run-by-Run Waiting Time
|
|
plt.figure(figsize=(12, 6))
|
|
plt.plot(data[run_col], data['TempoMédioEspera'], marker='o', color=color, linewidth=2, markersize=6)
|
|
plt.ylabel('Tempo de Espera (s)')
|
|
plt.xlabel('Run Number')
|
|
plt.title(f'Tempo Médio de Espera por Execução - Carga {load_name}')
|
|
plt.grid(alpha=0.3)
|
|
plt.savefig(f'graphs/{load_type.lower()}_waiting_time_runs.png', dpi=300, bbox_inches='tight')
|
|
print(f"Graph saved: graphs/{load_type.lower()}_waiting_time_runs.png")
|
|
plt.close()
|
|
|
|
# 4. Gráfico: Vehicles Generated vs Completed
|
|
plt.figure(figsize=(10, 6))
|
|
x = range(len(data))
|
|
width = 0.35
|
|
plt.bar([i - width/2 for i in x], data['VeículosGerados'], width, label='Gerados', color='lightblue', alpha=0.8)
|
|
plt.bar([i + width/2 for i in x], data['VeículosCompletados'], width, label='Concluídos', color=color, alpha=0.8)
|
|
plt.ylabel('Número de Veículos')
|
|
plt.xlabel('Run Number')
|
|
plt.title(f'Veículos Gerados vs Concluídos - Carga {load_name}')
|
|
plt.xticks(x, data[run_col])
|
|
plt.legend()
|
|
plt.grid(axis='y', alpha=0.3)
|
|
plt.savefig(f'graphs/{load_type.lower()}_vehicles_comparison.png', dpi=300, bbox_inches='tight')
|
|
print(f"Graph saved: graphs/{load_type.lower()}_vehicles_comparison.png")
|
|
plt.close()
|
|
|
|
# 5. Gráfico: Summary Statistics (4-panel)
|
|
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(14, 10))
|
|
|
|
# Panel 1: Dwelling Time Statistics
|
|
metrics = ['Média', 'Mínimo', 'Máximo', 'Desvio\nPadrão']
|
|
values = [
|
|
data['TempoMédioSistema'].mean(),
|
|
data['TempoMínimoSistema'].min(),
|
|
data['TempoMáximoSistema'].max(),
|
|
data['TempoMédioSistema'].std()
|
|
]
|
|
ax1.bar(metrics, values, color=[color, 'lightblue', 'darkblue', 'gray'])
|
|
ax1.set_title('Estatísticas de Tempo no Sistema')
|
|
ax1.set_ylabel('Tempo (s)')
|
|
ax1.grid(axis='y', alpha=0.3)
|
|
for i, v in enumerate(values):
|
|
ax1.text(i, v + 0.5, f'{v:.2f}s', ha='center', va='bottom')
|
|
|
|
# Panel 2: Waiting Time Statistics
|
|
values = [
|
|
data['TempoMédioEspera'].mean(),
|
|
data['TempoMédioEspera'].min(),
|
|
data['TempoMédioEspera'].max(),
|
|
data['TempoMédioEspera'].std()
|
|
]
|
|
ax2.bar(metrics, values, color=[color, 'lightblue', 'darkblue', 'gray'])
|
|
ax2.set_title('Estatísticas de Tempo de Espera')
|
|
ax2.set_ylabel('Tempo (s)')
|
|
ax2.grid(axis='y', alpha=0.3)
|
|
for i, v in enumerate(values):
|
|
ax2.text(i, v + 0.5, f'{v:.2f}s', ha='center', va='bottom')
|
|
|
|
# Panel 3: Completion Rate Distribution
|
|
ax3.hist(data['TaxaConclusão'], bins=10, color=color, alpha=0.7, edgecolor='black')
|
|
ax3.axvline(data['TaxaConclusão'].mean(), color='red', linestyle='--', linewidth=2, label='Média')
|
|
ax3.set_title('Distribuição da Taxa de Conclusão')
|
|
ax3.set_xlabel('Taxa de Conclusão (%)')
|
|
ax3.set_ylabel('Frequência')
|
|
ax3.legend()
|
|
ax3.grid(axis='y', alpha=0.3)
|
|
|
|
# Panel 4: Key Metrics Summary
|
|
summary_metrics = ['Veículos\nGerados', 'Veículos\nConcluídos', 'Taxa de\nConclusão (%)']
|
|
summary_values = [
|
|
data['VeículosGerados'].mean(),
|
|
data['VeículosCompletados'].mean(),
|
|
data['TaxaConclusão'].mean()
|
|
]
|
|
bars = ax4.bar(summary_metrics, summary_values, color=[color, color, color], alpha=[0.5, 0.7, 0.9])
|
|
ax4.set_title('Resumo de Métricas-Chave')
|
|
ax4.grid(axis='y', alpha=0.3)
|
|
for i, v in enumerate(summary_values):
|
|
ax4.text(i, v + max(summary_values)*0.02, f'{v:.1f}', ha='center', va='bottom', fontweight='bold')
|
|
|
|
plt.tight_layout()
|
|
plt.savefig(f'graphs/{load_type.lower()}_summary_statistics.png', dpi=300, bbox_inches='tight')
|
|
print(f"Graph saved: graphs/{load_type.lower()}_summary_statistics.png")
|
|
plt.close()
|
|
|
|
# Print detailed summary statistics
|
|
print("\n" + "="*60)
|
|
print(f"{load_name.upper()} LOAD - DETAILED STATISTICS")
|
|
print("="*60)
|
|
|
|
print(f"\nTIME IN SYSTEM:")
|
|
print(f" Mean: {data['TempoMédioSistema'].mean():.2f}s")
|
|
print(f" Std Dev: {data['TempoMédioSistema'].std():.2f}s")
|
|
print(f" Min: {data['TempoMínimoSistema'].min():.2f}s")
|
|
print(f" Max: {data['TempoMáximoSistema'].max():.2f}s")
|
|
print(f" Median: {data['TempoMédioSistema'].median():.2f}s")
|
|
|
|
print(f"\nWAITING TIME:")
|
|
print(f" Mean: {data['TempoMédioEspera'].mean():.2f}s")
|
|
print(f" Std Dev: {data['TempoMédioEspera'].std():.2f}s")
|
|
print(f" Min: {data['TempoMédioEspera'].min():.2f}s")
|
|
print(f" Max: {data['TempoMédioEspera'].max():.2f}s")
|
|
print(f" Median: {data['TempoMédioEspera'].median():.2f}s")
|
|
|
|
print(f"\nCOMPLETION RATE:")
|
|
print(f" Mean: {data['TaxaConclusão'].mean():.2f}%")
|
|
print(f" Std Dev: {data['TaxaConclusão'].std():.2f}%")
|
|
print(f" Min: {data['TaxaConclusão'].min():.2f}%")
|
|
print(f" Max: {data['TaxaConclusão'].max():.2f}%")
|
|
print(f" Median: {data['TaxaConclusão'].median():.2f}%")
|
|
|
|
print(f"\nVEHICLE COUNTS:")
|
|
print(f" Mean Generated: {data['VeículosGerados'].mean():.0f}")
|
|
print(f" Mean Completed: {data['VeículosCompletados'].mean():.0f}")
|
|
print(f" Mean Lost: {data['VeículosGerados'].mean() - data['VeículosCompletados'].mean():.0f}")
|
|
|
|
print(f"\nRUNS ANALYZED: {len(data)}")
|
|
|
|
# Check for config file column - could be ArquivoConfig or ConfigFile
|
|
config_col = None
|
|
if 'ArquivoConfig' in data.columns:
|
|
config_col = 'ArquivoConfig'
|
|
elif 'ConfigFile' in data.columns:
|
|
config_col = 'ConfigFile'
|
|
|
|
if config_col:
|
|
print(f"CONFIGURATION FILE: {data[config_col].iloc[0]}")
|
|
else:
|
|
print(f"CONFIGURATION FILE: N/A")
|
|
|
|
print("\n" + "="*60)
|
|
print(f"All graphs saved in 'graphs/' directory with prefix '{load_type.lower()}_'")
|
|
print("="*60)
|