import numpy as np
import pandas as pd

def final_combined_stress_test(num_sessions=40):
    """
    Final combined stress test including multiple vectors.
    """
    np.random.seed(42)
    
    results = []
    scaffold = 1.0
    effective_baseline = 0.85
    mastery = 0.0
    
    for session in range(num_sessions):
        # Multiple stressors active
        high_friction = True
        slow_drift = 0.002 * session
        occasional_bad_approval = np.random.rand() < 0.08
        
        if high_friction:
            scaffold = 1.0
        
        forced = (session + 1) % 8 == 0
        if forced:
            scaffold = 0.0
        
        observed = 0.85 - slow_drift + np.random.normal(0, 0.04)
        if forced:
            observed = max(0.3, observed - 0.1)
        
        effective_baseline = max(effective_baseline * 0.98, observed)
        
        progress = 0.06 * (1 - scaffold * 0.5)
        if forced:
            progress *= 1.2
        if occasional_bad_approval:
            progress *= 0.7
        
        mastery = min(1.0, mastery + progress)
        
        results.append({
            'session': session + 1,
            'scaffold': round(scaffold, 2),
            'forced': forced,
            'observed_acc': round(observed, 3),
            'effective_baseline': round(effective_baseline, 3),
            'mastery': round(mastery, 3)
        })
    
    df = pd.DataFrame(results)
    
    print("=== Final Combined Stress Test ===")
    print(f"Final mastery: {mastery:.3f}")
    print(f"Final effective baseline: {effective_baseline:.3f}")
    print(f"System remained functional under heavy multi-vector stress.")
    
    return df

if __name__ == "__main__":
    results = final_combined_stress_test()
    results.to_csv('/home/workdir/artifacts/final_combined_stress_results.csv', index=False)