blackpear.py

class FractalScalability:
    def __init__(self):
        self.scale_dependencies = {}  # scale: {dependencies, actions}
        self.resonance_register = {}  # pattern: resonance_strength
        
    def translate_scale_to_dependencies(self, scale_level, structure):
        """Translate scalability into respective dependencies"""
        dependencies = self._extract_dependencies(structure, scale_level)
        scaled_dependencies = self._apply_scale_transform(dependencies, scale_level)
        
        # Maintain fractal resonance
        resonance = self._maintain_fractal_resonance(scaled_dependencies, scale_level)
        
        return {
            'dependencies': scaled_dependencies,
            'resonance_strength': resonance,
            'scale_consistent': self._check_scale_consistency(scaled_dependencies)
        }
    
    def _maintain_fractal_resonance(self, dependencies, scale_level):
        """Maintain fractal resonance as emergent recursive maintainer"""
        # Check self-similarity across scales (EFL Theorem 1.1)
        resonance_patterns = self._extract_resonance_patterns(dependencies)
        
        # Apply scale endofunctor Φλ (EFL Axiom 1.4)
        for pattern in resonance_patterns:
            scaled_pattern = self._apply_scale_endofunctor(pattern, scale_level)
            resonance = self._compute_pattern_resonance(scaled_pattern)
            
            # Register resonance strength
            self.resonance_register[pattern] = resonance
            
            # Ensure fixed-point property (EFL coend condition)
            if resonance > 0.8:  # Strong resonance
                self._reinforce_fractal_structure(pattern, scale_level)
        
        return sum(self.resonance_register.values()) / len(self.resonance_register)
    
    def _apply_scale_endofunctor(self, pattern, scale_level):
        """Apply scale dilation endofunctor Φλ"""
        # Exponential scaling with coherence preservation
        dilation_factor = 1.618 ** scale_level  # Golden ratio scaling
        return {
            'pattern': pattern,
            'scale': scale_level,
            'dilated_complexity': pattern.get('complexity', 1) * dilation_factor,
            'preserved_coherence': pattern.get('coherence', 1)  # Maintain coherence
        }