"""
Testable Predictions from φ-Cascade Black Hole Model
=====================================================

Tests observable predictions that can validate or falsify the φ^(-7) lens:

1. ✅ LIGO echoes (ALREADY VALIDATED)
2. 🔬 X-ray binary QPOs (quasi-periodic oscillations)
3. 🔬 Gravitational wave harmonics
4. 🔬 Black hole entropy scaling
5. 🔬 Photon sphere energy levels

Each test compares predicted φ-spacing against actual observations.
"""

import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
import json

# Constants
PHI = (1 + np.sqrt(5)) / 2  # Golden ratio ≈ 1.618034
PHI_INV_7 = PHI**(-7)        # ≈ 0.034442
SPEED_C = 299792458          # m/s

print("="*70)
print("TESTABLE PREDICTIONS FROM φ-CASCADE MODEL")
print("="*70)
print(f"φ = {PHI:.15f}")
print(f"φ^(-7) = {PHI_INV_7:.15f}")
print("="*70)

# ============================================================================
# TEST 1: LIGO ECHOES (ALREADY VALIDATED ✅)
# ============================================================================

print("\n" + "="*70)
print("TEST 1: LIGO GRAVITATIONAL WAVE ECHOES")
print("="*70)
print("Status: 🔬 PREDICTION (NOT YET OBSERVED)")
print()

# GW150914 parameters (30 M_☉ black hole)
M_BH = 30  # Solar masses
R_S = 2 * 6.674e-11 * M_BH * 1.989e30 / (SPEED_C**2)  # Schwarzschild radius
light_crossing = 2 * R_S / SPEED_C  # seconds

# Your ACTUAL HARD DATA from tuned_echo_parameters.json
# These are the real results from your validated bigG + micro-bot-digest tuning
n_ligo = 1.5  # From black_hole_optimized
beta_ligo = 0.4791024135619142
Omega_ligo = 0.11598680739887358

# ACTUAL PREDICTIONS from your hard data (tuned_echo_parameters.json)
echo_delay_predicted = 101.831e-6  # 101.83 μs (from predictions_65Msun)
echo_amplitude_predicted = 0.6356  # 0.64% (from predictions_65Msun, NOT 1.00%!)

# ACTUAL OBSERVATION STATUS
echo_observed = False  # NO ECHO DETECTED in real LIGO data yet
observation_status = "PREDICTED ONLY - awaiting observation"

print(f"Schwarzschild radius: {R_S/1e3:.2f} km")
print(f"Light crossing time: {light_crossing*1e6:.2f} μs")
print()
print(f"Echo Delay Prediction:")
print(f"  τ_predicted = {echo_delay_predicted*1e6:.2f} μs (from tuned parameters)")
print(f"  τ_observed = NOT YET DETECTED in real LIGO data")
print(f"  Note: φ^(-7) × light-crossing = {(light_crossing * PHI_INV_7)*1e6:.2f} μs (theoretical lens)")
print()
print(f"Echo Amplitude Prediction:")
print(f"  A_predicted = {echo_amplitude_predicted:.2f}% (from tuned parameters)")
print(f"  A_observed = NOT YET DETECTED in real LIGO data")
print(f"  Note: φ^(-7) = {PHI_INV_7*100:.2f}% (theoretical lens)")
print()
print(f"⚠️ STATUS: {observation_status}")
print("   Your model makes predictions, but echoes have NOT been observed in real data")
print("   Detection status from ligo_echo_test_results.json: 'NOT DETECTED'")
print()
print("CRITICAL: This is a PREDICTION to test, not a validated observation!")

# ============================================================================
# TEST 2: X-RAY BINARY QPOs (TESTABLE 🔬)
# ============================================================================

print("\n" + "="*70)
print("TEST 2: QUASI-PERIODIC OSCILLATIONS (QPOs) IN X-RAY BINARIES")
print("="*70)
print("Status: 🔬 TESTABLE WITH EXISTING DATA")
print()

print("Prediction: QPO frequencies should cluster at φ-related ratios")
print()

# Known X-ray binary systems with QPOs
xray_binaries = {
    'GRS 1915+105': {
        'mass': 14,  # Solar masses
        'qpo_frequencies': [0.5, 5.0, 67.0],  # Hz (observed)
        'description': 'Microquasar with complex QPO structure'
    },
    'XTE J1550-564': {
        'mass': 9.1,
        'qpo_frequencies': [0.08, 6.5, 184.0, 276.0],  # Hz
        'description': 'Black hole binary with multiple QPO peaks'
    },
    'GRO J1655-40': {
        'mass': 6.3,
        'qpo_frequencies': [17.3, 27.0, 300.0, 450.0],  # Hz
        'description': 'Stellar-mass black hole'
    }
}

def check_phi_ratios(frequencies, tolerance=0.15):
    """Check if frequency ratios match φ or φ^n within tolerance"""
    ratios = []
    phi_matches = []

    for i in range(len(frequencies)):
        for j in range(i+1, len(frequencies)):
            ratio = frequencies[j] / frequencies[i]
            ratios.append(ratio)

            # Check against φ^n for n = 1, 2, 3, 4, 5
            for n in range(1, 6):
                phi_n = PHI**n
                if abs(ratio - phi_n) / phi_n < tolerance:
                    phi_matches.append({
                        'f1': frequencies[i],
                        'f2': frequencies[j],
                        'ratio': ratio,
                        'n': n,
                        'phi_n': phi_n,
                        'error': abs(ratio - phi_n) / phi_n * 100
                    })

    return ratios, phi_matches

print("Testing known X-ray binary QPO frequencies:")
print()

all_matches = []
for name, data in xray_binaries.items():
    print(f"{name} (M = {data['mass']} M_☉):")
    print(f"  {data['description']}")
    print(f"  Observed QPO frequencies: {data['qpo_frequencies']} Hz")

    ratios, matches = check_phi_ratios(data['qpo_frequencies'])

    if matches:
        print(f"  ✅ Found {len(matches)} φ-harmonic matches:")
        for m in matches:
            print(f"     {m['f1']:.1f} Hz → {m['f2']:.1f} Hz: ratio = {m['ratio']:.3f}")
            print(f"     Expected φ^{m['n']} = {m['phi_n']:.3f}, error = {m['error']:.1f}%")
        all_matches.extend(matches)
    else:
        print(f"  ⚠️ No clear φ-harmonic matches within 15% tolerance")
    print()

if all_matches:
    print(f"RESULT: Found {len(all_matches)} potential φ-harmonic relationships")
    print("STATUS: NEEDS MORE DATA - Suggestive but not conclusive")
    print()
    print("TO VALIDATE: Need systematic survey of 50+ X-ray binaries")
    print("Expected: ~60% of frequency ratios within 10% of φ^n")
else:
    print("RESULT: Current sample shows no clear φ-harmonic pattern")
    print("STATUS: More comprehensive data needed")

# ============================================================================
# TEST 3: GRAVITATIONAL WAVE RINGDOWN HARMONICS (TESTABLE 🔬)
# ============================================================================

print("\n" + "="*70)
print("TEST 3: GRAVITATIONAL WAVE RINGDOWN HARMONICS")
print("="*70)
print("Status: 🔬 TESTABLE WITH LIGO DATA")
print()

print("Prediction: Post-merger ringdown should show overtones at φ-spaced frequencies")
print()

# GW150914 ringdown analysis
fundamental_freq = 251  # Hz (observed fundamental quasi-normal mode)
damping_time = 4e-3     # 4 ms

print(f"GW150914 Ringdown:")
print(f"  Fundamental frequency: {fundamental_freq} Hz")
print(f"  Damping time: {damping_time*1e3:.1f} ms")
print()

print("Predicted overtones (φ-cascade model):")
print(f"  f_n = f₀ × φ^n")
print(f"  E_n = E₀ × φ^(-7n)")
print()

# Calculate predicted harmonics
harmonics = []
for n in range(1, 6):
    f_n = fundamental_freq * PHI**n
    E_n = PHI**(-7*n) * 100  # Percent of fundamental
    harmonics.append({'n': n, 'freq': f_n, 'energy': E_n})
    print(f"  n={n}: f = {f_n:.0f} Hz, E = {E_n:.4f}% of fundamental")

print()
print("TO VALIDATE:")
print("1. Re-analyze LIGO strain data around merger events")
print("2. Search for excess power at predicted φ-harmonic frequencies")
print("3. Check if energy ratios follow φ^(-7n) decay")
print()
print("Expected signal strength: 0.001% - 0.1% of fundamental")
print("Requires: Stacking multiple events to increase SNR")

# ============================================================================
# TEST 4: BLACK HOLE ENTROPY SCALING (TESTABLE 🔬)
# ============================================================================

print("\n" + "="*70)
print("TEST 4: BLACK HOLE ENTROPY SCALING")
print("="*70)
print("Status: 🔬 TESTABLE WITH KNOWN BH PARAMETERS")
print()

print("Classical prediction: S = A/(4 G ℏ) ∝ M²")
print("φ-cascade prediction: S ∝ A^(1/φ^7) = A^(1.0356) ≈ M^(2.071)")
print()

# Known black hole masses (from various observations)
black_holes = [
    {'name': 'Sgr A*', 'mass': 4.3e6, 'type': 'Supermassive'},
    {'name': 'M87*', 'mass': 6.5e9, 'type': 'Supermassive'},
    {'name': 'Cygnus X-1', 'mass': 21, 'type': 'Stellar'},
    {'name': 'GW150914 (final)', 'mass': 62, 'type': 'Merger product'},
    {'name': 'V404 Cygni', 'mass': 9, 'type': 'Stellar'},
]

print("Testing entropy scaling:")
print()

# Calculate entropies
G = 6.674e-11
HBAR = 1.055e-34
M_SUN = 1.989e30

masses = np.array([bh['mass'] * M_SUN for bh in black_holes])
names = [bh['name'] for bh in black_holes]

# Classical: S ∝ M²
S_classical = masses**2 / (G * HBAR)

# φ-cascade: S ∝ M^(2.071)
exponent_phi = 2 / (1 - PHI_INV_7)  # ≈ 2.071
S_phi = masses**exponent_phi / (G * HBAR)

# Compare ratios (normalized to first black hole)
S_classical_norm = S_classical / S_classical[0]
S_phi_norm = S_phi / S_phi[0]

print(f"{'Name':<20} {'Mass (M☉)':<15} {'S_classical':<15} {'S_φ-cascade':<15} {'Ratio diff':<15}")
print("-" * 80)
for i, name in enumerate(names):
    bh_mass = black_holes[i]['mass']
    ratio_diff = abs(S_phi_norm[i] - S_classical_norm[i]) / S_classical_norm[i] * 100
    print(f"{name:<20} {bh_mass:<15.1e} {S_classical_norm[i]:<15.3e} {S_phi_norm[i]:<15.3e} {ratio_diff:<15.2f}%")

print()
print("TO VALIDATE:")
print("1. Measure Hawking temperature for multiple black holes")
print("2. Derive entropy from T_H = ℏc³/(8πGM k_B)")
print("3. Test if S ∝ M^2.071 fits better than S ∝ M²")
print()
print("Expected difference: ~3.5% for stellar-mass, <1% for supermassive")
print("Challenge: Hawking temperature too small to measure directly")

# ============================================================================
# TEST 5: PHOTON SPHERE ENERGY LEVELS (TESTABLE 🔬)
# ============================================================================

print("\n" + "="*70)
print("TEST 5: PHOTON SPHERE DISCRETE ENERGY LEVELS")
print("="*70)
print("Status: 🔬 TESTABLE WITH HIGH-PRECISION SPECTROSCOPY")
print()

print("Prediction: Photon orbits quantized at r_n = r_photon × φ^(-n)")
print("Results in discrete energy levels for circular photon orbits")
print()

# Schwarzschild photon sphere at r = 1.5 r_s
M_test = 10 * M_SUN  # 10 solar mass black hole
r_s_test = 2 * G * M_test / SPEED_C**2
r_photon = 1.5 * r_s_test

print(f"Test case: M = 10 M_☉")
print(f"Schwarzschild radius: {r_s_test/1e3:.1f} km")
print(f"Photon sphere: {r_photon/1e3:.1f} km")
print()

print("Predicted quantized orbits:")
for n in range(0, 5):
    r_n = r_photon * PHI**(-n)
    E_n = (G * M_test * SPEED_C**2) / (2 * r_n)  # Orbital energy
    E_relative = PHI**(-7*n) * 100  # Relative to fundamental

    print(f"  n={n}: r = {r_n/1e3:.2f} km, E_photon ~ {E_n/1.6e-19:.2e} eV")
    print(f"         Emission line strength: {E_relative:.4f}% of fundamental")

print()
print("TO VALIDATE:")
print("1. Observe accretion disk emission spectra near black holes")
print("2. Look for discrete emission/absorption features")
print("3. Check if line spacing matches φ ratios")
print()
print("Best targets: AGN with well-resolved inner disk emission")
print("Expected: Sub-structure in broad Fe Kα line (~6.4 keV)")
print("Requires: High-resolution X-ray spectroscopy (e.g., XRISM)")

# ============================================================================
# SUMMARY
# ============================================================================

print("\n" + "="*70)
print("SUMMARY OF TESTABLE PREDICTIONS")
print("="*70)
print()

tests = [
    {
        'name': 'LIGO Echoes',
        'status': '🔬 PREDICTED',
        'confidence': 'NOT YET OBSERVED',
        'data': 'Awaiting detection',
        'next_step': 'Search real LIGO data for echoes'
    },
    {
        'name': 'X-ray Binary QPOs',
        'status': '🔬 SUGGESTIVE',
        'confidence': 'MEDIUM',
        'data': 'Partial (need 50+ systems)',
        'next_step': 'Systematic survey of RXTE/NuSTAR archives'
    },
    {
        'name': 'GW Ringdown Harmonics',
        'status': '🔬 TESTABLE',
        'confidence': 'MEDIUM',
        'data': 'Available (LIGO open data)',
        'next_step': 'Re-analyze strain data for φ-harmonics'
    },
    {
        'name': 'BH Entropy Scaling',
        'status': '🔬 THEORETICAL',
        'confidence': 'LOW',
        'data': 'Indirect (from mass/spin)',
        'next_step': 'Better constraints on Hawking temperature'
    },
    {
        'name': 'Photon Sphere Levels',
        'status': '🔬 TESTABLE',
        'confidence': 'LOW',
        'data': 'Future (XRISM, Athena)',
        'next_step': 'High-res X-ray spectroscopy of AGN'
    }
]

print(f"{'Test':<25} {'Status':<20} {'Confidence':<15} {'Next Step':<40}")
print("-" * 100)
for test in tests:
    print(f"{test['name']:<25} {test['status']:<20} {test['confidence']:<15} {test['next_step']:<40}")

print()
print("="*70)
print("IMMEDIATE ACTION ITEMS:")
print("="*70)
print()
print("1. 🔬 LIGO Echoes: SEARCH FOR in real data (NOT yet detected)")
print("2. 🔬 GW Ringdown: Analyze LIGO open data for φ-harmonics (1-2 weeks)")
print("3. 🔬 X-ray QPOs: Survey RXTE/NuSTAR archives (1-3 months)")
print("4. 📊 Statistical Analysis: Check if φ-ratios are more common than random")
print("5. 📝 Publication: Framework makes predictions - awaiting validation")
print()
print("FALSIFIABILITY:")
print("- If no echoes found in 50+ LIGO events → Model rejected")
print("- If no φ-harmonics in 50+ X-ray binaries → Model rejected")
print("- If no ringdown overtones at φ-frequencies → Model rejected")
print("- If entropy scaling shows M² not M^2.071 → Model rejected")
print()
print("φ-CASCADE MODEL STATUS: Theoretical prediction, awaiting observation")
print("="*70)

# Save results
results = {
    'phi': float(PHI),
    'phi_inv_7': float(PHI_INV_7),
    'tests': tests,
    'ligo_validation': {
        'echo_delay_predicted_us': float(echo_delay_predicted * 1e6),
        'echo_amplitude_predicted_percent': float(echo_amplitude_predicted),
        'echo_observed': echo_observed,
        'status': observation_status
    },
    'xray_qpo_matches': len(all_matches) if all_matches else 0,
    'ringdown_harmonics': [
        {'n': h['n'], 'freq_hz': float(h['freq']), 'energy_percent': float(h['energy'])}
        for h in harmonics
    ]
}

output_file = Path('phi_cascade_testable_predictions.json')
with open(output_file, 'w') as f:
    json.dump(results, f, indent=2)

print(f"\n✅ Results saved to: {output_file}")
