MCP Memory Service

#!/usr/bin/env python3 """ Generate statistical visualizations for MCP Memory Service repository. This script creates charts from CSV data exports to visualize: - Monthly commit and release trends - Activity patterns by hour and day of week - Contributor breakdown - October 2025 sprint visualization Usage: python generate_charts.py Output: PNG files in docs/statistics/charts/ """ import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from pathlib import Path import numpy as np # Set style sns.set_style("whitegrid") plt.rcParams['figure.figsize'] = (12, 6) plt.rcParams['font.size'] = 10 # Paths DATA_DIR = Path(__file__).parent / "data" CHARTS_DIR = Path(__file__).parent / "charts" CHARTS_DIR.mkdir(exist_ok=True) def create_monthly_activity_chart(): """Create dual-axis chart showing commits and releases over time.""" df = pd.read_csv(DATA_DIR / "monthly_activity.csv") fig, ax1 = plt.subplots(figsize=(14, 7)) # Commits line color = 'tab:blue' ax1.set_xlabel('Month', fontsize=12, fontweight='bold') ax1.set_ylabel('Commits', color=color, fontsize=12, fontweight='bold') ax1.plot(df['month'], df['commits'], color=color, marker='o', linewidth=2.5, markersize=8, label='Commits') ax1.tick_params(axis='y', labelcolor=color) ax1.grid(True, alpha=0.3) # Releases bars ax2 = ax1.twinx() color = 'tab:orange' ax2.set_ylabel('Releases', color=color, fontsize=12, fontweight='bold') ax2.bar(df['month'], df['releases'], color=color, alpha=0.6, label='Releases') ax2.tick_params(axis='y', labelcolor=color) # Title and formatting plt.title('MCP Memory Service - Monthly Activity (Dec 2024 - Oct 2025)', fontsize=14, fontweight='bold', pad=20) # Rotate x-axis labels ax1.set_xticklabels(df['month'], rotation=45, ha='right') # Add legends lines1, labels1 = ax1.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left', fontsize=10) # Highlight October 2025 oct_idx = df[df['month'] == '2025-10'].index[0] ax1.axvspan(oct_idx - 0.4, oct_idx + 0.4, alpha=0.2, color='red', label='October Sprint') plt.tight_layout() plt.savefig(CHARTS_DIR / "monthly_activity.png", dpi=300, bbox_inches='tight') print("✅ Created: monthly_activity.png") plt.close() def create_activity_heatmap(): """Create heatmap showing activity by hour and day of week.""" # Read hourly data hourly_df = pd.read_csv(DATA_DIR / "activity_by_hour.csv") daily_df = pd.read_csv(DATA_DIR / "activity_by_day.csv") # Create a simulated day x hour matrix (for visualization purposes) # In reality, we'd need actual day+hour data from git log days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] hours = range(24) # Create visualization showing just hourly distribution fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10)) # Hourly activity bar chart ax1.bar(hourly_df['hour'], hourly_df['commits'], color='steelblue', alpha=0.8) ax1.set_xlabel('Hour of Day', fontsize=12, fontweight='bold') ax1.set_ylabel('Number of Commits', fontsize=12, fontweight='bold') ax1.set_title('Activity by Hour of Day', fontsize=14, fontweight='bold', pad=15) ax1.grid(axis='y', alpha=0.3) # Highlight peak hours (20-22) peak_hours = [20, 21, 22] for hour in peak_hours: idx = hourly_df[hourly_df['hour'] == hour].index[0] ax1.bar(hour, hourly_df.loc[idx, 'commits'], color='red', alpha=0.7) ax1.text(21, 170, 'Peak Hours\n(19:00-23:00)\n46% of commits', ha='center', va='bottom', fontsize=11, bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5)) # Day of week activity day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] daily_sorted = daily_df.set_index('day_of_week').loc[day_order].reset_index() colors = ['steelblue' if day not in ['Saturday', 'Sunday'] else 'orange' for day in daily_sorted['day_of_week']] ax2.barh(daily_sorted['day_of_week'], daily_sorted['commits'], color=colors, alpha=0.8) ax2.set_xlabel('Number of Commits', fontsize=12, fontweight='bold') ax2.set_ylabel('Day of Week', fontsize=12, fontweight='bold') ax2.set_title('Activity by Day of Week', fontsize=14, fontweight='bold', pad=15) ax2.grid(axis='x', alpha=0.3) # Add percentage labels for idx, row in daily_sorted.iterrows(): ax2.text(row['commits'] + 5, idx, row['percentage'], va='center', fontsize=10) ax2.text(250, 5.5, 'Weekend\nWarrior\n39% total', ha='center', va='center', fontsize=11, bbox=dict(boxstyle='round', facecolor='orange', alpha=0.3)) plt.tight_layout() plt.savefig(CHARTS_DIR / "activity_patterns.png", dpi=300, bbox_inches='tight') print("✅ Created: activity_patterns.png") plt.close() def create_contributor_pie_chart(): """Create pie chart showing contributor distribution.""" df = pd.read_csv(DATA_DIR / "contributors.csv") # Combine Henry, doobidoo, Heinrich Krupp (same person) primary_commits = df[df['contributor'].isin(['Henry', 'doobidoo', 'Heinrich Krupp'])]['commits'].sum() other_commits = df[~df['contributor'].isin(['Henry', 'doobidoo', 'Heinrich Krupp'])]['commits'].sum() labels = [f'Primary Maintainer\n(Henry + aliases)', 'External Contributors'] sizes = [primary_commits, other_commits] colors = ['#FF9999', '#66B2FF'] explode = (0.1, 0) fig, ax = plt.subplots(figsize=(10, 8)) wedges, texts, autotexts = ax.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, textprops={'fontsize': 12, 'fontweight': 'bold'}) # Make percentage text larger for autotext in autotexts: autotext.set_color('white') autotext.set_fontsize(14) autotext.set_fontweight('bold') ax.set_title('Contributor Distribution (1,536 total commits)', fontsize=14, fontweight='bold', pad=20) # Add legend with individual contributors top_contributors = df.head(10) legend_labels = [f"{row['contributor']}: {row['commits']} ({row['percentage']})" for _, row in top_contributors.iterrows()] plt.legend(legend_labels, title="Top 10 Contributors", loc='center left', bbox_to_anchor=(1, 0, 0.5, 1), fontsize=9) plt.tight_layout() plt.savefig(CHARTS_DIR / "contributors.png", dpi=300, bbox_inches='tight') print("✅ Created: contributors.png") plt.close() def create_october_sprint_chart(): """Create detailed visualization of October 2025 sprint.""" # October daily data (from earlier analysis) oct_days = [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] oct_commits = [16, 46, 26, 9, 2, 14, 9, 1, 7, 13, 3, 12, 4, 5, 5, 9, 15, 16, 5, 38, 5, 24, 1, 12, 12] fig, ax = plt.subplots(figsize=(16, 7)) # Bar chart bars = ax.bar(oct_days, oct_commits, color='steelblue', alpha=0.8) # Highlight the sprint days (28-31) sprint_days = [28, 29, 30, 31] for i, day in enumerate(oct_days): if day in sprint_days: bars[i].set_color('red') bars[i].set_alpha(0.9) ax.set_xlabel('Day of October 2025', fontsize=12, fontweight='bold') ax.set_ylabel('Number of Commits', fontsize=12, fontweight='bold') ax.set_title('October 2025: The Sprint Month (310 commits, 65 releases)', fontsize=14, fontweight='bold', pad=20) ax.grid(axis='y', alpha=0.3) # Add annotations for key days ax.annotate('Peak Day\n46 commits', xy=(3, 46), xytext=(3, 52), ha='center', fontsize=10, fontweight='bold', bbox=dict(boxstyle='round', facecolor='yellow', alpha=0.5), arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')) ax.annotate('13 Releases\nin 4 Days', xy=(29.5, 35), xytext=(29.5, 42), ha='center', fontsize=11, fontweight='bold', bbox=dict(boxstyle='round', facecolor='red', alpha=0.3), arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')) # Add text box with sprint details sprint_text = 'Oct 28-31 Sprint:\n• v8.12.0 → v8.15.1\n• 13 releases\n• 49 commits\n• Production bugs fixed' ax.text(8, 40, sprint_text, fontsize=11, bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.5)) plt.tight_layout() plt.savefig(CHARTS_DIR / "october_sprint.png", dpi=300, bbox_inches='tight') print("✅ Created: october_sprint.png") plt.close() def create_growth_trajectory(): """Create cumulative commits chart showing growth over time.""" df = pd.read_csv(DATA_DIR / "monthly_activity.csv") # Calculate cumulative commits df['cumulative_commits'] = df['commits'].cumsum() df['cumulative_releases'] = df['releases'].cumsum() fig, ax1 = plt.subplots(figsize=(14, 7)) # Cumulative commits color = 'tab:blue' ax1.set_xlabel('Month', fontsize=12, fontweight='bold') ax1.set_ylabel('Cumulative Commits', color=color, fontsize=12, fontweight='bold') ax1.plot(df['month'], df['cumulative_commits'], color=color, marker='o', linewidth=3, markersize=8, label='Cumulative Commits') ax1.tick_params(axis='y', labelcolor=color) ax1.grid(True, alpha=0.3) ax1.fill_between(range(len(df)), df['cumulative_commits'], alpha=0.3, color=color) # Cumulative releases ax2 = ax1.twinx() color = 'tab:green' ax2.set_ylabel('Cumulative Releases', color=color, fontsize=12, fontweight='bold') ax2.plot(df['month'], df['cumulative_releases'], color=color, marker='s', linewidth=3, markersize=8, label='Cumulative Releases', linestyle='--') ax2.tick_params(axis='y', labelcolor=color) # Title plt.title('MCP Memory Service - Growth Trajectory (10 Months)', fontsize=14, fontweight='bold', pad=20) # Rotate labels ax1.set_xticklabels(df['month'], rotation=45, ha='right') # Add milestone annotations ax1.annotate('First Release\nv1.0', xy=(0, 55), xytext=(1, 200), ha='center', fontsize=10, arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.3')) ax1.annotate('1,000th\nCommit', xy=(8, 1000), xytext=(7, 1200), ha='center', fontsize=10, arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=-0.3')) # Legends lines1, labels1 = ax1.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left', fontsize=10) plt.tight_layout() plt.savefig(CHARTS_DIR / "growth_trajectory.png", dpi=300, bbox_inches='tight') print("✅ Created: growth_trajectory.png") plt.close() def main(): """Generate all charts.""" print("🎨 Generating statistical visualizations...") print() create_monthly_activity_chart() create_activity_heatmap() create_contributor_pie_chart() create_october_sprint_chart() create_growth_trajectory() print() print("✅ All charts generated successfully!") print(f"📁 Output directory: {CHARTS_DIR}") print() print("Generated charts:") print(" 1. monthly_activity.png - Commits and releases over time") print(" 2. activity_patterns.png - Hourly and daily patterns") print(" 3. contributors.png - Contributor distribution") print(" 4. october_sprint.png - October 2025 detailed view") print(" 5. growth_trajectory.png - Cumulative growth") if __name__ == "__main__": main()