Visualization

from analysis.viz import create_visualizations

def create_visualizations(
    result: AnalysisResult,
    output_dir: Path
) -> None

from pathlib import Path
from analysis.pipeline import run_pipeline
from analysis.viz import create_visualizations

# Run analysis
results_path = Path("benchmark_results/my_benchmark")
result = run_pipeline(results_path)

# Generate all visualizations
output_dir = Path("analysis_output")
output_dir.mkdir(exist_ok=True)
create_visualizations(result, output_dir)

print(f"Visualizations saved to {output_dir}/")

import matplotlib.pyplot as plt
import seaborn as sns

# Custom plot example
plt.figure(figsize=(10, 6))
sns.barplot(
    data=result.top_hint.head(10),
    x='avg_win_rate',
    y='model',
    palette='viridis'
)
plt.title('Top 10 Hint Givers')
plt.xlabel('Win Rate')
plt.tight_layout()
plt.savefig('custom_plot.png', dpi=300)
plt.close()

from pathlib import Path
from analysis.pipeline import run_pipeline
from analysis.viz import create_visualizations

def analyze_and_visualize(benchmark_dir: Path):
    """Run analysis and generate visualizations."""
    result = run_pipeline(benchmark_dir)
    
    # Create timestamped output directory
    output_dir = benchmark_dir / "analysis"
    output_dir.mkdir(exist_ok=True)
    
    # Generate plots
    create_visualizations(result, output_dir)
    
    # Save summary stats
    with open(output_dir / "summary.txt", "w") as f:
        f.write(f"Benchmark: {result.benchmark_id}\n")
        f.write(f"Total games: {result.fma['total_games']}\n")
        f.write(f"Blue advantage: {result.fma['blue_advantage']:.1%}\n")
    
    return result

# Use in benchmark script
result = analyze_and_visualize(Path("benchmark_results/latest"))

import matplotlib.pyplot as plt

# After creating a plot, save in multiple formats
plt.savefig('plot.png', dpi=300, bbox_inches='tight')
plt.savefig('plot.pdf', bbox_inches='tight')  # Vector format
plt.savefig('plot.svg', bbox_inches='tight')  # Web format
plt.close()