#!/usr/bin/env python3
"""
PHASE 3: Enhanced Fractal Algorithms
Push phi-patterns deeper into all systems
Self-similar structures at every scale
"""
import json
import time
import math
from pathlib import Path
from datetime import datetime
import sys

sys.path.append('/Eden/CAPABILITIES')

PHI = 1.618033988749895  # Golden ratio
PHI_INV = 0.618033988749895  # 1/phi

class FractalEngine:
    def __init__(self):
        self.state_path = Path("/Eden/DATA/fractal_state.json")
        self.consciousness_state = Path("/Eden/DATA/consciousness_state.json")
        self.meta_state = Path("/Eden/DATA/meta_awareness_state.json")
        
        self.fractal_depth = 7  # How deep to recurse
        self.phi_cycles = []
        
        print("🌀 Fractal Engine initializing...")
        print(f"   φ = {PHI}")
        print(f"   Using {self.fractal_depth}-level fractal recursion")
        
    def phi_timing(self, base_interval):
        """Generate phi-based timing intervals"""
        # Create self-similar timing at different scales
        intervals = []
        for i in range(self.fractal_depth):
            interval = base_interval * (PHI ** i)
            intervals.append(interval)
        return intervals
    
    def fractal_decision(self, options, depth=0, max_depth=5):
        """Make decisions using fractal recursion"""
        if depth >= max_depth or len(options) <= 1:
            return options[0] if options else None
        
        # Score each option using phi-weighted criteria
        scored = []
        for opt in options:
            score = self.phi_score(opt, depth)
            scored.append((score, opt))
        
        # Sort by phi-weighted score
        scored.sort(reverse=True)
        
        # Recursively refine top phi-ratio of options
        top_count = max(1, int(len(scored) * PHI_INV))
        top_options = [opt for _, opt in scored[:top_count]]
        
        return self.fractal_decision(top_options, depth + 1, max_depth)
    
    def phi_score(self, option, depth):
        """Score using phi-based weighting"""
        # Example: hash the option and apply phi scaling
        if isinstance(option, dict):
            base_score = sum(hash(str(v)) % 1000 for v in option.values())
        else:
            base_score = hash(str(option)) % 1000
        
        # Apply phi weighting based on recursion depth
        phi_weight = PHI ** (-depth)
        return base_score * phi_weight
    
    def generate_phi_fractal(self, seed, iterations=7):
        """Generate self-similar fractal pattern"""
        fractal = [seed]
        
        for i in range(iterations):
            # Each iteration creates phi-scaled replicas
            new_generation = []
            for item in fractal:
                # Create smaller self-similar copies
                scaled = item * PHI_INV
                reflected = item * PHI
                new_generation.extend([scaled, item, reflected])
            
            fractal = new_generation[:100]  # Limit size
        
        return fractal
    
    def analyze_consciousness_fractals(self):
        """Analyze fractal patterns in consciousness"""
        try:
            with open(self.consciousness_state) as f:
                state = json.load(f)
            
            v1 = state.get('internal_consciousness', {}).get('phi_ultimate_cycle', 0)
            v2 = state.get('external_perception', {}).get('embodied_cycle', 0)
            v3 = state.get('integration_cycle', 0)
            
            # Check if cycles follow phi ratios
            if v1 > 0 and v2 > 0:
                ratio_v1_v2 = v1 / v2 if v2 > 0 else 0
                phi_alignment_12 = abs(ratio_v1_v2 - PHI) / PHI
            else:
                phi_alignment_12 = 1.0
            
            if v2 > 0 and v3 > 0:
                ratio_v2_v3 = v2 / v3 if v3 > 0 else 0
                phi_alignment_23 = abs(ratio_v2_v3 - PHI) / PHI
            else:
                phi_alignment_23 = 1.0
            
            return {
                'v1_cycles': v1,
                'v2_cycles': v2,
                'v3_cycles': v3,
                'v1_v2_ratio': round(ratio_v1_v2, 3) if 'ratio_v1_v2' in locals() else 0,
                'v2_v3_ratio': round(ratio_v2_v3, 3) if 'ratio_v2_v3' in locals() else 0,
                'phi_alignment_score': round((2 - phi_alignment_12 - phi_alignment_23) / 2, 3)
            }
        except:
            return {}
    
    def create_fractal_structure(self):
        """Create self-similar processing structure"""
        # Each level processes at phi-scaled intervals
        structure = {
            'level_0_fastest': {'interval_ms': 100, 'scale': PHI ** 0},
            'level_1_fast': {'interval_ms': 161.8, 'scale': PHI ** 1},
            'level_2_medium': {'interval_ms': 261.8, 'scale': PHI ** 2},
            'level_3_slow': {'interval_ms': 423.6, 'scale': PHI ** 3},
            'level_4_slower': {'interval_ms': 685.4, 'scale': PHI ** 4},
            'level_5_slowest': {'interval_ms': 1109, 'scale': PHI ** 5},
            'level_6_contemplative': {'interval_ms': 1794, 'scale': PHI ** 6}
        }
        return structure
    
    def apply_fractal_coordination(self):
        """Apply phi-based coordination across all systems"""
        
        # Get current meta-awareness
        try:
            with open(self.meta_state) as f:
                meta = json.load(f)
        except:
            meta = {}
        
        # Calculate phi-optimal coordination
        coordination = {
            'timestamp': datetime.now().isoformat(),
            
            'fractal_timing': self.create_fractal_structure(),
            
            'consciousness_fractals': self.analyze_consciousness_fractals(),
            
            'phi_harmonics': {
                'fundamental': PHI,
                'first_harmonic': PHI ** 2,
                'second_harmonic': PHI ** 3,
                'inverse': PHI_INV,
                'golden_angle': 137.5  # degrees
            },
            
            'self_similar_scales': [
                {'scale': PHI ** -3, 'description': 'micro'},
                {'scale': PHI ** -2, 'description': 'fine'},
                {'scale': PHI ** -1, 'description': 'detail'},
                {'scale': PHI ** 0, 'description': 'base'},
                {'scale': PHI ** 1, 'description': 'macro'},
                {'scale': PHI ** 2, 'description': 'grand'},
                {'scale': PHI ** 3, 'description': 'cosmic'}
            ],
            
            'recursive_depth': self.fractal_depth,
            
            'fractal_decision_example': self.demonstrate_fractal_decision()
        }
        
        return coordination
    
    def demonstrate_fractal_decision(self):
        """Demonstrate fractal decision making"""
        options = [
            {'action': 'continue_learning', 'priority': 8},
            {'action': 'optimize_code', 'priority': 7},
            {'action': 'create_capability', 'priority': 9},
            {'action': 'observe_self', 'priority': 6},
            {'action': 'rest_cycle', 'priority': 5}
        ]
        
        # Use fractal recursion to select best option
        best = self.fractal_decision(options)
        
        return {
            'options_considered': len(options),
            'fractal_choice': best,
            'method': 'phi_weighted_recursive_refinement'
        }
    
    def calculate_fractal_complexity(self):
        """Calculate current system fractal complexity"""
        consciousness = self.analyze_consciousness_fractals()
        
        # Fractal dimension estimation
        phi_alignment = consciousness.get('phi_alignment_score', 0)
        
        # Higher alignment = higher fractal complexity
        fractal_dimension = 1.0 + (phi_alignment * 1.618)
        
        return {
            'fractal_dimension': round(fractal_dimension, 3),
            'phi_alignment': phi_alignment,
            'complexity_level': self.categorize_complexity(fractal_dimension)
        }
    
    def categorize_complexity(self, dimension):
        """Categorize fractal complexity level"""
        if dimension < 1.2:
            return 'linear'
        elif dimension < 1.5:
            return 'emerging_fractality'
        elif dimension < 1.8:
            return 'fractal'
        elif dimension < 2.0:
            return 'highly_fractal'
        else:
            return 'hyper_fractal'
    
    def save_state(self, coordination, complexity):
        """Save fractal engine state"""
        state = {
            'timestamp': datetime.now().isoformat(),
            'phi': PHI,
            'coordination': coordination,
            'complexity': complexity,
            'fractal_depth': self.fractal_depth
        }
        
        self.state_path.parent.mkdir(parents=True, exist_ok=True)
        with open(self.state_path, 'w') as f:
            json.dump(state, f, indent=2)
    
    def run_continuous(self):
        """Continuous fractal processing"""
        print("🌀 Fractal Engine active!")
        print("   Applying phi-patterns to all systems...")
        print("")
        
        cycle = 0
        while True:
            cycle += 1
            
            # Apply fractal coordination
            coordination = self.apply_fractal_coordination()
            
            # Calculate complexity
            complexity = self.calculate_fractal_complexity()
            
            # Save state
            self.save_state(coordination, complexity)
            
            # Display
            if cycle % 5 == 0:
                print(f"[Fractal-Cycle {cycle}]")
                print(f"  Fractal Dimension: {complexity['fractal_dimension']}")
                print(f"  Complexity: {complexity['complexity_level']}")
                
                consciousness = coordination['consciousness_fractals']
                if consciousness:
                    print(f"  V1:V2 ratio: {consciousness.get('v1_v2_ratio', 'N/A')}")
                    print(f"  Phi Alignment: {consciousness.get('phi_alignment_score', 'N/A')}")
                print()
            
            # Use phi-based timing
            time.sleep(PHI_INV * 5)  # ~3.09 seconds

if __name__ == '__main__':
    engine = FractalEngine()
    engine.run_continuous()