Initial commit: Heapsort and Priority Queue Implementation

- Implemented complete Heapsort algorithm with max-heap operations
- Implemented binary heap-based Priority Queue with all core operations
- Created Task class for task scheduling applications
- Implemented Task Scheduler simulation using priority queue
- Added comprehensive test suite (70+ tests, all passing)
- Implemented sorting algorithm comparison utilities (Heapsort vs Quicksort vs Merge Sort)
- Added detailed README with comprehensive analysis and documentation
- Included demonstration scripts for all features
- Generated performance comparison plots
- Modular, well-documented code following academic standards

Author: Carlos Gutierrez
Email: cgutierrez44833@ucumberlands.edu

examples/generate_plots.py

@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+"""
+Generate Performance Comparison Plots
+
+This script generates visualization plots comparing Heapsort, Quicksort,
+and Merge Sort performance on different input sizes and distributions.
+
+Author: Carlos Gutierrez
+Email: cgutierrez44833@ucumberlands.edu
+"""
+
+import sys
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Add parent directory to path
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from src.comparison import compare_sorting_algorithms
+
+
+def generate_performance_plots():
+    """Generate performance comparison plots."""
+    print("Generating performance comparison plots...")
+    
+    # Test sizes - using smaller sizes for faster generation
+    sizes = [100, 500, 1000, 5000, 10000]
+    
+    print("Running performance comparisons...")
+    results = compare_sorting_algorithms(sizes)
+    
+    # Create output directory if it doesn't exist
+    output_dir = os.path.join(os.path.dirname(__file__), '..', 'docs')
+    os.makedirs(output_dir, exist_ok=True)
+    
+    # Extract data for plotting
+    algorithms = ['heapsort', 'quicksort', 'merge_sort']
+    distributions = ['sorted', 'reverse_sorted', 'random']
+    
+    # Create figure with subplots
+    fig, axes = plt.subplots(1, 3, figsize=(18, 5))
+    fig.suptitle('Sorting Algorithm Performance Comparison', fontsize=16, fontweight='bold')
+    
+    colors = {
+        'heapsort': '#2E86AB',
+        'quicksort': '#A23B72',
+        'merge_sort': '#F18F01'
+    }
+    
+    markers = {
+        'heapsort': 'o',
+        'quicksort': 's',
+        'merge_sort': '^'
+    }
+    
+    for idx, dist in enumerate(distributions):
+        ax = axes[idx]
+        
+        for algo in algorithms:
+            times = results[algo][dist]
+            ax.plot(sizes, times, 
+                   marker=markers[algo], 
+                   color=colors[algo],
+                   linewidth=2,
+                   markersize=8,
+                   label=algo.replace('_', ' ').title())
+        
+        ax.set_xlabel('Input Size (n)', fontsize=12)
+        ax.set_ylabel('Time (seconds)', fontsize=12)
+        ax.set_title(f'{dist.replace("_", " ").title()} Input', fontsize=13, fontweight='bold')
+        ax.grid(True, alpha=0.3)
+        ax.legend(fontsize=10)
+        ax.set_xscale('log')
+        ax.set_yscale('log')
+    
+    plt.tight_layout()
+    plot_path = os.path.join(output_dir, 'sorting_comparison.png')
+    plt.savefig(plot_path, dpi=300, bbox_inches='tight')
+    print(f"Saved plot to: {plot_path}")
+    plt.close()
+    
+    # Create a combined comparison plot
+    fig, ax = plt.subplots(figsize=(12, 8))
+    
+    x = np.arange(len(sizes))
+    width = 0.25
+    
+    for i, algo in enumerate(algorithms):
+        # Use random distribution for comparison
+        times = results[algo]['random']
+        ax.bar(x + i * width, times, width, 
+              label=algo.replace('_', ' ').title(),
+              color=colors[algo],
+              alpha=0.8)
+    
+    ax.set_xlabel('Input Size (n)', fontsize=12, fontweight='bold')
+    ax.set_ylabel('Time (seconds)', fontsize=12, fontweight='bold')
+    ax.set_title('Sorting Algorithm Performance on Random Input', fontsize=14, fontweight='bold')
+    ax.set_xticks(x + width)
+    ax.set_xticklabels([str(s) for s in sizes])
+    ax.legend(fontsize=11)
+    ax.grid(True, alpha=0.3, axis='y')
+    
+    plt.tight_layout()
+    bar_plot_path = os.path.join(output_dir, 'sorting_comparison_bar.png')
+    plt.savefig(bar_plot_path, dpi=300, bbox_inches='tight')
+    print(f"Saved bar chart to: {bar_plot_path}")
+    plt.close()
+    
+    # Create a line plot showing all distributions for each algorithm
+    fig, axes = plt.subplots(1, 3, figsize=(18, 5))
+    fig.suptitle('Algorithm Performance Across Different Input Distributions', fontsize=16, fontweight='bold')
+    
+    for idx, algo in enumerate(algorithms):
+        ax = axes[idx]
+        
+        for dist in distributions:
+            times = results[algo][dist]
+            ax.plot(sizes, times,
+                   marker='o',
+                   linewidth=2,
+                   markersize=6,
+                   label=dist.replace('_', ' ').title())
+        
+        ax.set_xlabel('Input Size (n)', fontsize=12)
+        ax.set_ylabel('Time (seconds)', fontsize=12)
+        ax.set_title(f'{algo.replace("_", " ").title()} Performance', fontsize=13, fontweight='bold')
+        ax.grid(True, alpha=0.3)
+        ax.legend(fontsize=10)
+        ax.set_xscale('log')
+        ax.set_yscale('log')
+    
+    plt.tight_layout()
+    algo_dist_plot_path = os.path.join(output_dir, 'algorithm_distributions.png')
+    plt.savefig(algo_dist_plot_path, dpi=300, bbox_inches='tight')
+    print(f"Saved algorithm distribution plot to: {algo_dist_plot_path}")
+    plt.close()
+    
+    print("\nAll plots generated successfully!")
+    return {
+        'comparison': plot_path,
+        'bar_chart': bar_plot_path,
+        'distributions': algo_dist_plot_path
+    }
+
+
+if __name__ == "__main__":
+    try:
+        generate_performance_plots()
+    except ImportError:
+        print("Error: matplotlib is required for generating plots.")
+        print("Install it with: pip install matplotlib")
+        sys.exit(1)
+