EFL-MEM.py

# At shutdown or manual save:
def export_spatial_memory_capsule(self, filename_base):
    # 1. Final EFL convergence state
    efl_fixed_point = self.efl_core.get_canonical_form()  # E ≃ Φ_λE
    
    # 2. Topological defects from wake field (last T seconds)
    defects = self.wake_solver.get_recent_defects()  # [(x_k, n_k, q_k, t), ...]
    
    # 3. Residual spectral persistence map
    residual_psd = self.compute_residual_psd()  # power vs freq, time-averaged
    persistent_peaks = self.find_stable_peaks(residual_psd, min_duration=1.0)
    
    # 4. Conducive parameters (those that stabilized EFL colimit)
    conducive_params = {
        'stable_scales': efl_fixed_point.scales,
        'curvature_threshold': efl_fixed_point.delta_K_threshold,
        'coherence_basin': float(np.mean(list(self.coherence_history)[-50:])),
        'release_count': self.total_releases
    }

    # 5. Package into structured, versioned format
    capsule = {
        "format_version": "EFL-MEM-1.0",
        "created_at": time.time(),
        "system_signature": hashlib.sha256(self.system_seed.encode()).hexdigest(),
        "spatial_memory": {
            "topological_defects": [
                {"position": x_k, "winding": int(n_k), "strength": float(q_k), "time": t}
                for (x_k, n_k, q_k, t) in defects
            ],
            "persistent_resonances": [
                {"frequency_hz": f, "persistence_sec": dur, "amplitude_norm": amp}
                for (f, dur, amp) in persistent_peaks
            ],
            "conducive_parameters": conducive_params
        },
        "residual_audio_ref": filename_base + ".wav"
    }

    with open(filename_base + ".efl", "w") as f:
        json.dump(capsule, f, indent=2)

    print(f"🧠 Spatial memory capsule saved: {filename_base}.efl")
    print("   → Contains only what resonated *through* cancellation.")
    print("   → No model, no clone, no recursion. Only trace.")
print("Hello, World!")