import numpy as np
import pandas as pd

def run_erb_v64_final_audit():
    # ══════════════════════════════════════════════════════════════════════════
    # L0: TOPOLOGISCHE HARDWARE (Axiome)
    # ══════════════════════════════════════════════════════════════════════════
    NG, NB, Q = 3, 2, 1                   # 3 Gen, 2 Branes, 1 Baryon-Unit
    XI = 1.5                              # Der 3/2 Attraktor
    GAMMA = 2 * np.pi                     # Q=1 Vortex-Schließung
    
    # Nackte Bessel-Nullstellen (SO(3) Resonanz)
    R_BARE = np.array([np.pi, 4.493409, 5.763459])
    
    # ══════════════════════════════════════════════════════════════════════════
    # L1/L2: KONSTANTEN (Die Brane-Software)
    # ══════════════════════════════════════════════════════════════════════════
    delta = XI - 1.0                      # 0.5 (Isospin-Strain)
    theta_B = delta / (NG * NB * GAMMA)   # Berry-Einheit (0.5 / 12pi)
    vasym = 1 - (delta / 48)              # Vakuum-Dämpfung
    g = (XI/(1+XI)) * (delta/(XI+1)) * vasym # Universelle Kopplung (0.1187)
    
    # Dressing: Verschiebung der Knoten (Renormierung)
    dr = theta_B / (NG * np.pi)           # Delta r ≈ 0.0014
    R = np.array([R_BARE[0], R_BARE[1] + dr, R_BARE[2] - dr])
    
    # epsilon_0: Die Basis-Tension des Vakuums
    beta = GAMMA / (GAMMA - 1)
    eps0 = (g / np.sqrt(3.0)) * (1 + theta_B * (beta**2 / XI + XI * theta_B))

    # ══════════════════════════════════════════════════════════════════════════
    # L3/L4: MASSEN-SKALA & AUTARKIE
    # ══════════════════════════════════════════════════════════════════════════
    mp = 0.93827 # GeV (Proton-Anker)
    Lambda = (mp * XI**2 * GAMMA) / (2 * g**2) # Gesamte Brane-Energie
    
    mt = Lambda / np.exp(1)               # Top-Masse (Euler Decay)
    mc = mt / (R[2] / R[1])**(NB * np.pi**2) # Volume Scaling
    mu = (mc / (R[1] / R[0])**(NG * GAMMA - 1)) * 1000 # Surface Scaling (in MeV)
    
    # Bosonen
    mW = Lambda * np.sqrt(g) * delta
    sin2w = (g / delta) * (1 - theta_B)
    mZ = (mW / np.sqrt(1 - sin2w)) * (1 - theta_B / 4)
    mH = mW * (np.pi / 2) * (1 - theta_B / 1.2)

    # ══════════════════════════════════════════════════════════════════════════
    # L5: CKM ASYMMETRIC MATRIX (Chau-Keung Projection)
    # ══════════════════════════════════════════════════════════════════════════
    # Winkel-Ableitungen aus Geometrie
    t12 = np.arcsin(eps0 * np.pi * (1 + (NG/NB) * theta_B))
    t23 = np.arcsin((g / NG) * (1 + theta_B))
    t13 = np.arcsin((eps0**2) * (3.0 / 4.0)) # Casimir-Seal (3/4 Korrektur)
    
    dcp = np.radians(60.0) # Hexagonal Lock-in
    
    s12, c12 = np.sin(t12), np.cos(t12)
    s23, c23 = np.sin(t23), np.cos(t23)
    s13, c13 = np.sin(t13), np.cos(t13)
    eid = np.exp(1j * dcp)
    
    # Aufbau der komplexen Matrix
    V = np.array([
        [c12*c13,                      s12*c13,                      s13*np.exp(-1j*dcp)],
        [-s12*c23 - c12*s23*s13*eid,   c12*c23 - s12*s23*s13*eid,    s23*c13            ],
        [s12*s23 - c12*c23*s13*eid,    -c12*s23 - s12*c23*s13*eid,   c23*c13            ]
    ])
    CKM = np.abs(V)
    J = s12 * s23 * s13 * c12 * c23 * (c13**2) * np.sin(dcp)

    # ══════════════════════════════════════════════════════════════════════════
    # L6: PMNS NEUTRINOS
    # ══════════════════════════════════════════════════════════════════════════
    th23_p = np.degrees(np.arccos(1 / XI))
    th12_p = np.degrees(np.arcsin(R[0] / R[2]))
    th13_p = th23_p * g * np.sqrt(XI)

    # ══════════════════════════════════════════════════════════════════════════
    # AUSGABE-GENERIERUNG
    # ══════════════════════════════════════════════════════════════════════════
    print("\n" + "═"*75)
    print(" ERB DIAMOND MASTER ENGINE v64 — FINAL ALGEBRAIC CLOSURE")
    print("═"*75)
    
    # 1. CKM Matrix Tabelle
    print("\nI. VOLLSTÄNDIGE CKM MATRIX (Beträge)")
    print("         d           s           b")
    q_labels = ['u', 'c', 't']
    for i, row in enumerate(CKM):
        print(f"{q_labels[i]}  " + "  ".join([f"{x:10.6f}" for x in row]))
    
    # 2. Vergleichs-Tabelle
    print("\nII. PARAMETER-VERGLEICH (ERB vs PDG)")
    results = [
        ("m_u (MeV)", mu, 2.16), ("m_c (GeV)", mc, 1.275), ("m_t (GeV)", mt, 172.76),
        ("M_W (GeV)", mW, 80.38), ("M_Z (GeV)", mZ, 91.19), ("M_H (GeV)", mH, 125.1),
        ("V_us (CKM)", CKM[0,1], 0.2250), ("V_cb (CKM)", CKM[1,2], 0.0410),
        ("V_ub (CKM)", CKM[0,2], 0.0037), ("V_td (CKM)", CKM[2,0], 0.0086),
        ("Jarlskog J", J, 3.0e-5), ("th23_pmns", th23_p, 48.2), ("th12_pmns", th12_p, 33.4)
    ]
    df = pd.DataFrame(results, columns=["Parameter", "ERB v64", "PDG"])
    df["Match %"] = (1 - np.abs(df["ERB v64"] - df["PDG"]) / df["PDG"]) * 100
    print(df.to_string(index=False, formatters={'ERB v64': '{:,.5f}'.format, 'Match %': '{:,.2f}%'.format}))

    print("\n" + "─"*75)
    print(f"Unitaritäts-Check (Summe Zeile 1²): {np.sum(CKM[0]**2):.12f}")
    print(f"Vtd/Vub Asymmetrie-Ratio:        {CKM[2,0]/CKM[0,2]:.3f} (PDG ~2.3)")
    print(f"Topologische Verschiebung Δr:     {dr:.6f}")
    print("═"*75)

if __name__ == "__main__":
    run_erb_v64_final_audit()