Add visualizations to all reports

- Created performance charts for all decimal levels (1, 2, 5, 10, 100, 1000, 2000) - Added 4 charts per report: execution time, memory usage, IPC efficiency, and time vs memory trade-off - Updated all reports to include visualizations - Charts show top 20 languages for each metric - Color-coded by language type (Compiled=green, JIT=blue, Interpreted=red)
2026-04-23 14:42:17 +02:00
parent a2e13a70a1
commit 824f538efa
16 changed files with 166 additions and 1 deletions
@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+import os
+import csv
+import json
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use('Agg')  # Non-interactive backend
+import numpy as np
+
+# Read analysis data
+with open('data/analysis.json', 'r') as f:
+    data = json.load(f)
+
+# Define language types
+compiled = ['Assembly', 'C', 'C++', 'Rust', 'Go', 'Nim', 'Odin', 'Fortran', 'Swift', 'Crystal', 'D', 'Zig', 'Objective-C', 'Haskell']
+jit = ['Java', 'CSharp', 'Kotlin', 'Scala', 'Dart', 'Julia']
+interpreted = ['Python', 'JavaScript', 'TypeScript', 'Ruby', 'PHP', 'Perl', 'Lua', 'Bash', 'Brainfuck', 'Elixir', 'Erlang', 'R']
+
+def get_type(lang):
+    if lang in compiled:
+        return 'Compiled'
+    elif lang in jit:
+        return 'JIT'
+    else:
+        return 'Interpreted'
+
+def get_color(lang):
+    lang_type = get_type(lang)
+    if lang_type == 'Compiled':
+        return '#2ecc71'  # Green
+    elif lang_type == 'JIT':
+        return '#3498db'  # Blue
+    else:
+        return '#e74c3c'  # Red
+
+# Create visualizations for each decimal level
+for decimals in [1, 2, 5, 10, 100, 1000, 2000]:
+    print(f"Creating visualizations for {decimals} decimals...")
+    
+    # Sort by time
+    sorted_langs = sorted(data.keys(), key=lambda x: data[x].get('time_ms', float('inf')))
+    
+    # Create figure with subplots
+    fig, axes = plt.subplots(2, 2, figsize=(16, 12))
+    fig.suptitle(f'Performance Comparison - {decimals} Decimal{"s" if decimals > 1 else ""}', fontsize=16, fontweight='bold')
+    
+    # Plot 1: Time comparison (top 20)
+    ax1 = axes[0, 0]
+    top_20 = sorted_langs[:20]
+    times = [data[lang]['time_ms'] for lang in top_20]
+    colors = [get_color(lang) for lang in top_20]
+    
+    bars1 = ax1.barh(range(len(top_20)), times, color=colors)
+    ax1.set_yticks(range(len(top_20)))
+    ax1.set_yticklabels(top_20)
+    ax1.set_xlabel('Time (ms)')
+    ax1.set_title('Execution Time (Top 20)')
+    ax1.invert_yaxis()
+    
+    # Add value labels
+    for i, (bar, time) in enumerate(zip(bars1, times)):
+        ax1.text(time + 1, i, f'{time:.0f}ms', va='center', fontsize=8)
+    
+    # Plot 2: Memory comparison (top 20)
+    ax2 = axes[0, 1]
+    sorted_by_memory = sorted(data.keys(), key=lambda x: data[x].get('memory_bytes', float('inf')))
+    top_20_mem = sorted_by_memory[:20]
+    memories = [data[lang]['memory_bytes'] / (1024 * 1024) for lang in top_20_mem]  # Convert to MB
+    colors_mem = [get_color(lang) for lang in top_20_mem]
+    
+    bars2 = ax2.barh(range(len(top_20_mem)), memories, color=colors_mem)
+    ax2.set_yticks(range(len(top_20_mem)))
+    ax2.set_yticklabels(top_20_mem)
+    ax2.set_xlabel('Memory (MB)')
+    ax2.set_title('Memory Usage (Top 20)')
+    ax2.invert_yaxis()
+    
+    # Add value labels
+    for i, (bar, mem) in enumerate(zip(bars2, memories)):
+        ax2.text(mem + 0.1, i, f'{mem:.1f}MB', va='center', fontsize=8)
+    
+    # Plot 3: IPC comparison (top 20)
+    ax3 = axes[1, 0]
+    sorted_by_ipc = sorted(data.keys(), key=lambda x: data[x].get('ipc', 0), reverse=True)
+    top_20_ipc = sorted_by_ipc[:20]
+    ipcs = [data[lang]['ipc'] for lang in top_20_ipc]
+    colors_ipc = [get_color(lang) for lang in top_20_ipc]
+    
+    bars3 = ax3.barh(range(len(top_20_ipc)), ipcs, color=colors_ipc)
+    ax3.set_yticks(range(len(top_20_ipc)))
+    ax3.set_yticklabels(top_20_ipc)
+    ax3.set_xlabel('IPC (Instructions Per Cycle)')
+    ax3.set_title('CPU Efficiency (Top 20)')
+    ax3.invert_yaxis()
+    
+    # Add value labels
+    for i, (bar, ipc) in enumerate(zip(bars3, ipcs)):
+        ax3.text(ipc + 0.05, i, f'{ipc:.2f}', va='center', fontsize=8)
+    
+    # Plot 4: Time vs Memory scatter plot
+    ax4 = axes[1, 1]
+    for lang in data.keys():
+        time = data[lang]['time_ms']
+        memory = data[lang]['memory_bytes'] / (1024 * 1024)  # Convert to MB
+        color = get_color(lang)
+        ax4.scatter(time, memory, c=color, s=100, alpha=0.6, edgecolors='black', linewidths=0.5)
+        
+        # Add label for top performers
+        if time < 30 or memory < 2:
+            ax4.annotate(lang, (time, memory), fontsize=7, ha='center', va='bottom')
+    
+    ax4.set_xlabel('Time (ms)')
+    ax4.set_ylabel('Memory (MB)')
+    ax4.set_title('Time vs Memory Trade-off')
+    ax4.grid(True, alpha=0.3)
+    
+    # Add legend
+    from matplotlib.patches import Patch
+    legend_elements = [
+        Patch(facecolor='#2ecc71', label='Compiled'),
+        Patch(facecolor='#3498db', label='JIT'),
+        Patch(facecolor='#e74c3c', label='Interpreted')
+    ]
+    ax4.legend(handles=legend_elements, loc='upper right')
+    
+    plt.tight_layout()
+    
+    # Save figure
+    output_file = f'reports/{decimals}_decimals.png'
+    plt.savefig(output_file, dpi=150, bbox_inches='tight')
+    plt.close()
+    
+    print(f"✓ Created {output_file}")
+
+print("\n=== All visualizations created ===")