#!/usr/bin/env python3 # ============================================================================== # COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) # PROJECT: SOVEREIGN STACK # This artifact is entirely proprietary and cryptographically proven. # Open-Source usage requires explicit permission from Brandon Scott Schneider. # ============================================================================== """ Deterministic test of hybrid DSP workload selector using research space recordings. Tests the hybrid decision system on real audio recordings to measure: - DSP workload distribution (Raw, SpectralFocus, TransientEdge, Hybrid) - QUBO solve time reduction - Energy efficiency improvements """ import json import sys from pathlib import Path REPO_ROOT = Path(__file__).resolve().parents[1] sys.path.insert(0, str(REPO_ROOT)) from scripts.pipewire_waveprobe_compression_chain import run_chain # Test audio files from research space (WAV only, wave module doesn't support FLAC) TEST_AUDIO = [ REPO_ROOT / "media" / "test_audio" / "mandelbrot_boundary.wav", # Juliet fractal ] def test_baseline(audio_path: Path) -> dict: """Run baseline test without hybrid DSP selection.""" print(f"\n{'='*70}") print(f"BASELINE TEST: {audio_path.name}") print(f"{'='*70}") result = run_chain( wav_path=audio_path, chunk_size=4096, stride=2048, max_states=100, low_mi_threshold=0.1, high_mi_threshold=0.5, dsp_workload="hybrid", # Fixed hybrid workload method_profile="baseline", qubo_solver="anneal", qubo_anneal_steps=32, qubo_anneal_temp_start=0.5, qubo_anneal_temp_end=0.05, qubo_anneal_guidance="dsp_guided", qubo_benchmark_guidance_ablation=False, qubo_collect_exact_reference=False, hybrid_dsp_workload=False, # Baseline: no adaptive selection ) return result def test_hybrid(audio_path: Path) -> dict: """Run hybrid test with adaptive DSP workload selection.""" print(f"\n{'='*70}") print(f"HYBRID TEST: {audio_path.name}") print(f"{'='*70}") result = run_chain( wav_path=audio_path, chunk_size=4096, stride=2048, max_states=100, low_mi_threshold=0.1, high_mi_threshold=0.5, dsp_workload="hybrid", # Initial workload, will be overridden method_profile="baseline", qubo_solver="anneal", qubo_anneal_steps=32, qubo_anneal_temp_start=0.5, qubo_anneal_temp_end=0.05, qubo_anneal_guidance="dsp_guided", qubo_benchmark_guidance_ablation=False, qubo_collect_exact_reference=False, hybrid_dsp_workload=True, # Enable adaptive selection ) return result def compare_results(baseline: dict, hybrid: dict, audio_name: str) -> dict: """Compare baseline and hybrid results.""" baseline_summary = baseline["summary"] hybrid_summary = hybrid["summary"] comparison = { "audio_file": audio_name, "baseline_qubo_solve_time_ms": baseline_summary.get("avg_qubo_solve_time_ms", 0), "hybrid_qubo_solve_time_ms": hybrid_summary.get("avg_qubo_solve_time_ms", 0), "baseline_qubo_invocations": baseline_summary.get("qubo_invocations", 0), "hybrid_qubo_invocations": hybrid_summary.get("qubo_invocations", 0), "baseline_dsp_workload": baseline_summary.get("dsp_frontend", {}).get("workload", "unknown"), "hybrid_dsp_workload_distribution": hybrid_summary.get("dsp_workload_counts", {}), } # Calculate energy efficiency improvement if comparison["baseline_qubo_solve_time_ms"] > 0: time_reduction = ( (comparison["baseline_qubo_solve_time_ms"] - comparison["hybrid_qubo_solve_time_ms"]) / comparison["baseline_qubo_solve_time_ms"] ) * 100 comparison["qubo_time_reduction_pct"] = time_reduction else: comparison["qubo_time_reduction_pct"] = 0.0 return comparison def main(): print("="*70) print("Hybrid DSP Workload Selector Deterministic Test") print("="*70) print(f"\nTesting {len(TEST_AUDIO)} audio files from research space...") all_results = [] for audio_path in TEST_AUDIO: if not audio_path.exists(): print(f"⚠ Skipping {audio_path.name} (file not found)") continue if audio_path.suffix.lower() != ".wav": print(f"⚠ Skipping {audio_path.name} (not WAV format)") continue # Run baseline test baseline = test_baseline(audio_path) # Run hybrid test hybrid = test_hybrid(audio_path) # Compare results comparison = compare_results(baseline, hybrid, audio_path.name) all_results.append(comparison) # Print comparison print(f"\n{'='*70}") print(f"COMPARISON: {audio_path.name}") print(f"{'='*70}") print(f" Baseline QUBO solve time: {comparison['baseline_qubo_solve_time_ms']:.2f} ms") print(f" Hybrid QUBO solve time: {comparison['hybrid_qubo_solve_time_ms']:.2f} ms") print(f" QUBO time reduction: {comparison['qubo_time_reduction_pct']:+.2f}%") print(f" Baseline QUBO invocations: {comparison['baseline_qubo_invocations']}") print(f" Hybrid QUBO invocations: {comparison['hybrid_qubo_invocations']}") print(f" Hybrid DSP workload distribution:") for workload, count in comparison['hybrid_dsp_workload_distribution'].items(): total = sum(comparison['hybrid_dsp_workload_distribution'].values()) pct = (count / total * 100) if total > 0 else 0 print(f" {workload}: {count} ({pct:.1f}%)") # Print aggregate results print(f"\n{'='*70}") print("AGGREGATE RESULTS") print(f"{'='*70}") if all_results: avg_time_reduction = sum(r["qubo_time_reduction_pct"] for r in all_results) / len(all_results) print(f" Average QUBO time reduction: {avg_time_reduction:+.2f}%") print(f" Files tested: {len(all_results)}") # Aggregate DSP workload distribution total_distribution = {} for result in all_results: for workload, count in result["hybrid_dsp_workload_distribution"].items(): total_distribution[workload] = total_distribution.get(workload, 0) + count print(f"\n Aggregate DSP workload distribution:") total_workloads = sum(total_distribution.values()) for workload, count in sorted(total_distribution.items()): pct = (count / total_workloads * 100) if total_workloads > 0 else 0 print(f" {workload}: {count} ({pct:.1f}%)") # Save results to JSON output_path = REPO_ROOT / "out" / "hybrid_dsp_deterministic_test.json" output_path.parent.mkdir(parents=True, exist_ok=True) with open(output_path, "w") as f: json.dump({ "aggregate": { "avg_qubo_time_reduction_pct": avg_time_reduction, "total_files_tested": len(all_results), "aggregate_dsp_workload_distribution": total_distribution, }, "individual_results": all_results, }, f, indent=2) print(f"\n Results saved to: {output_path}") return 0 if __name__ == "__main__": sys.exit(main())