Einstein–Daramola Full Angular Sweep

import numpy as np

def run_comprehensive_lab_sweep(num_solitons=1_000_000):
    np.random.seed(42)

    # Define physical laboratory test orientation angles
    angles_deg = np.array([0.0, 15.0, 22.5, 30.0, 45.0, 60.0, 75.0, 90.0])
    angles_rad = np.radians(angles_deg)

    # Constant parameters based on Phase I & III framework limits
    Qf = 2.0
    E0_nanojoules = 1.602   # Baseline vacuum substrate energy density per soliton

    print("=" * 91)
    print("  EINSTEIN-DARAMOLA 3-PHASE LOCAL HIDDEN WEIGHT THEORY: COMPLETE LAB SWEEP")
    print("=" * 91)
    print()
    print(f"{'Lab Angle (θ)'}<15}{'Coincidences'}<15}{'Efficiency %'}<15}"
          f"{'Observed E(A,B)'}<20}{'Substrate Energy'}<20}")
    print("-" * 91)

    # PHASE I: Inject uniform, phase-locked soliton hidden voltage profiles (lambda)
    lam = np.random.uniform(0, 2 * np.pi, num_solitons)
    theta_A = 0.0   # Anchor Detector A

    for deg, theta_B in zip(angles_deg, angles_rad):
        # PHASE II: Boundary Layer ASP3 Torsion — 3D spatial phase tracking
        alpha_A = lam - theta_A
        alpha_B = lam - theta_B
        voltage_polarity_A = np.sign(np.cos(alpha_A))
        voltage_polarity_B = np.sign(np.cos(alpha_B))

        # PHASE III: Local threshold-driven mass-energy density calculations
        weight_density_A = np.abs(np.cos(alpha_A))
        weight_density_B = np.abs(np.cos(alpha_B))

        # Discrete sensor trigger evaluation
        click_A = np.random.rand(num_solitons) < weight_density_A
        click_B = np.random.rand(num_solitons) < weight_density_B
        coincidences = click_A & click_B
        total_coinc = np.sum(coincidences)
        efficiency = (total_coinc / num_solitons) * 100

        E_AB = (np.mean(voltage_polarity_A[coincidences] * voltage_polarity_B[coincidences])
                if total_coinc > 0 else 0.0)

        # Thermodynamic Track — external parametric energy drawn from substrate
        mean_gain_A = 1.0 + (Qf - 1.0) * np.mean(weight_density_A[coincidences] ** 2)
        mean_gain_B = 1.0 + (Qf - 1.0) * np.mean(weight_density_B[coincidences] ** 2)
        total_energy_uJ = total_coinc * E0_nanojoules * (mean_gain_A + mean_gain_B) / 1e3

        print(f"{deg:5.1f}°{''}<9}{total_coinc}<15,}{efficiency:5.2f}%{''}<9}"
              f"{E_AB:12.4f}{''}<8}{total_energy_uJ:12.4f} uJ")

    print("=" * 91)

if __name__ == "__main__":
    run_comprehensive_lab_sweep()