"""
CRITICAL ANALYSIS: Did We Cheat in the Scale-Dependent Model?
============================================================

This is a rigorous self-examination of potential methodological issues,
circular reasoning, or unfair advantages in the scale_dependent_model.py
"""

import numpy as np
import json

def analyze_potential_cheating():
    print("🔍 CRITICAL ANALYSIS: POTENTIAL CHEATING ASSESSMENT")
    print("=" * 65)

    # Load the results to examine them critically
    with open('scale_dependent_results.json', 'r') as f:
        results = json.load(f)

    print("\n1. METHODOLOGY EXAMINATION")
    print("-" * 50)

    print("POTENTIAL ISSUES TO INVESTIGATE:")
    print("❓ Are we fitting too many parameters to too little data?")
    print("❓ Is the φ-normalization artificially forcing good results?")
    print("❓ Are we cherry-picking favorable black hole data?")
    print("❓ Is the interpolation method biased?")
    print("❓ Are we over-fitting by adjusting both M and f?")

    print("\n2. DEGREES OF FREEDOM ANALYSIS")
    print("-" * 50)

    # Count parameters vs data points
    black_holes = len(results['true_black_hole_attributes'])
    fitted_params = 4  # n, β, Ω, k for black hole scale
    data_points = black_holes * 2  # M and f for each BH

    print(f"Data points: {data_points} (5 BH × 2 observables)")
    print(f"Fitted parameters: {fitted_params} (n, β, Ω, k)")
    print(f"Degrees of freedom: {data_points - fitted_params}")

    if data_points <= fitted_params:
        print("🚨 WARNING: Too many parameters for available data!")
    else:
        print("✓ Sufficient degrees of freedom for valid fitting")

    print("\n3. CIRCULAR REASONING CHECK")
    print("-" * 50)

    print("EXAMINING POTENTIAL CIRCULAR LOGIC:")

    # Check if we're fitting parameters and then using them to "validate" the same data
    print("❓ Do we fit BH parameters using BH data, then claim success on same data?")
    print("   → YES: We fit (n,β,Ω,k) to minimize φ-error on BH observations")
    print("   → THEN: We use those same parameters to find 'true' BH attributes")
    print("   → RESULT: Perfect φ^n alignment is GUARANTEED by construction!")

    print("\n🚨 MAJOR CONCERN: This could be circular reasoning!")

    print("\n4. φ-NORMALIZATION BIAS ANALYSIS")
    print("-" * 50)

    # Examine how φ-normalization works
    PHI = (1 + np.sqrt(5)) / 2

    print("φ-normalization forces ratios toward nearest φ^n:")
    test_ratios = [1.5, 2.7, 4.2, 6.9, 11.1, 50.0, 100.0]

    for ratio in test_ratios:
        n_closest = int(round(np.log(ratio) / np.log(PHI)))
        phi_n = PHI ** n_closest
        error = abs(ratio - phi_n) / phi_n
        print(f"  Ratio {ratio:5.1f} → φ^{n_closest} = {phi_n:5.2f}, error = {error:.4f}")

    print("\n❓ Does this mean ANY ratio can be made to look φ-aligned?")
    print("   → Large ratios have many φ^n options nearby!")

    print("\n5. INTERPOLATION BIAS CHECK")
    print("-" * 50)

    # Check if interpolation is reasonable
    micro = results['scale_parameters']['micro']
    cosmic = results['scale_parameters']['cosmic']
    bh = results['black_hole_parameters']

    # Calculate where BH parameters fall in interpolation
    log_micro = -15  # log10(1e-15)
    log_cosmic = np.log10(50)
    log_bh = np.log10(10.26)  # typical BH mass

    t_expected = (log_bh - log_micro) / (log_cosmic - log_micro)

    # Compare with actual parameter positions
    t_n = (bh['n'] - micro['n']) / (cosmic['n'] - micro['n'])
    t_Omega = (bh['Omega'] - micro['Omega']) / (cosmic['Omega'] - micro['Omega'])

    print(f"Expected interpolation factor: {t_expected:.3f}")
    print(f"Actual n interpolation: {t_n:.3f}")
    print(f"Actual Ω interpolation: {t_Omega:.3f}")

    if abs(t_n - t_expected) < 0.1 and abs(t_Omega - t_expected) < 0.1:
        print("✓ Parameters close to expected interpolation")
    else:
        print("❓ Parameters deviate significantly from pure interpolation")

    print("\n6. CHERRY-PICKING ASSESSMENT")
    print("-" * 50)

    print("Black hole sample used:")
    for attr in results['true_black_hole_attributes']:
        print(f"  {attr['name']}: {attr['M_classical']:.1f} M☉, {attr['f_classical']:.0f} Hz")

    print("\n❓ Are these representative or cherry-picked?")
    print("❓ What about black holes that don't fit well?")
    print("❓ Did we exclude any problematic data?")

    print("\n7. OVER-FITTING ANALYSIS")
    print("-" * 50)

    # Check if adjusting both M and f creates artificial success
    corrections = [(attr['M_correction_%'], attr['f_correction_%'])
                   for attr in results['true_black_hole_attributes']]

    print("Mass and frequency corrections applied:")
    for i, (dM, df) in enumerate(corrections):
        name = results['true_black_hole_attributes'][i]['name']
        print(f"  {name}: ΔM = {dM:+5.1f}%, Δf = {df:+5.1f}%")

    # Check if both are being adjusted in same direction
    M_adjustments = [c[0] for c in corrections]
    f_adjustments = [c[1] for c in corrections]

    print(f"\nMass correction stats: mean = {np.mean(M_adjustments):+.1f}%, std = {np.std(M_adjustments):.1f}%")
    print(f"Frequency correction stats: mean = {np.mean(f_adjustments):+.1f}%, std = {np.std(f_adjustments):.1f}%")

    if abs(np.mean(M_adjustments)) > 5 or abs(np.mean(f_adjustments)) > 5:
        print("🚨 WARNING: Systematic bias in corrections!")

    print("\n8. VALIDATION INDEPENDENCE CHECK")
    print("-" * 50)

    print("❓ Do we have independent validation data?")
    print("   → NO: All validation uses the same 5 BH we fitted to")
    print("   → This is NOT independent validation!")

    print("❓ Can we predict NEW black hole properties?")
    print("   → UNKNOWN: We haven't tested on unseen data")

    print("\n" + "=" * 65)
    print("🎯 CHEATING ASSESSMENT VERDICT")
    print("=" * 65)

    print("\nPOTENTIAL ISSUES IDENTIFIED:")
    print("🔴 CIRCULAR REASONING: Fitting parameters to data, then 'validating' on same data")
    print("🔴 GUARANTEED SUCCESS: φ-normalization + parameter fitting = automatic alignment")
    print("🔴 NO INDEPENDENT VALIDATION: All tests use the training data")
    print("🔴 CHERRY-PICKED DATA: Only 5 favorable black holes used")
    print("🔴 OVER-PARAMETERIZATION: 4 parameters fit to 5 data points")

    print("\nVALID ASPECTS:")
    print("✓ Mathematical framework is self-consistent")
    print("✓ Parameter scaling trends are physically reasonable")
    print("✓ Interpolation method is scientifically sound")
    print("✓ Framework equations are well-defined")

    print("\n🚨 OVERALL ASSESSMENT: PARTIALLY VALID BUT WITH SERIOUS CONCERNS")

    print("\nTO REMOVE 'CHEATING' SUSPICION, WE NEED:")
    print("1. Independent test data (new black holes not used in fitting)")
    print("2. Comparison with null hypothesis (random parameters)")
    print("3. Cross-validation (fit to subset, test on remainder)")
    print("4. Larger dataset to justify 4-parameter model")
    print("5. A priori predictions without post-hoc fitting")

if __name__ == '__main__':
    analyze_potential_cheating()