efl.py

class EmergentFractalProgrammer:
    def __init__(self):
        self.trinary_logic = TrinaryLogic()
        self.programmer = EmergentProgrammer()
        self.scalability = FractalScalability()
        self.fractal_reference = FractalReferenceMaintainer()
    
    def process_creative_directive(self, conditions, parameters, target_scale):
        """Complete creative process with fractal resonance maintenance"""
        # Step 1: Trinary state evaluation
        state_analysis = self.trinary_logic.evaluate(conditions, parameters)
        
        # Step 2: Discover new programming actions
        discovered_actions = self.programmer.discover_from_conditions(
            conditions, parameters, target_scale
        )
        
        # Step 3: Translate scalability to dependencies
        scale_translation = self.scalability.translate_scale_to_dependencies(
            target_scale, state_analysis
        )
        
        # Step 4: Maintain fractal resonance recursively
        resonance_maintained = self.fractal_reference.maintain_recursive_reference(
            state_analysis, discovered_actions, scale_translation
        )
        
        return {
            'state': state_analysis,
            'discovered_actions': discovered_actions,
            'scale_dependencies': scale_translation,
            'fractal_resonance': resonance_maintained,
            'emergence_level': self._compute_emergence_level(resonance_maintained)
        }

class FractalReferenceMaintainer:
    """Maintains fractal resonance as emergent recursive reference"""
    
    def maintain_recursive_reference(self, state, actions, scale_data):
        """Recursively maintain fractal reference across scales"""
        reference_network = self._build_reference_network(state, actions)
        
        # Apply recursive maintenance (EFL Proposition 1.1 - Transfinite Self-Similarity)
        for scale in range(len(scale_data['dependencies'])):
            self._apply_recursive_maintenance(reference_network, scale)
        
        return self._compute_resonance_stability(reference_network)
    
    def _apply_recursive_maintenance(self, network, scale_level):
        """Apply recursive maintenance at given scale"""
        # Each scale maintains references to others (fractal structure)
        for node in network:
            node['scale_references'] = [
                ref for ref in network 
                if ref['scale'] != scale_level 
                and self._check_fractal_similarity(node, ref)
            ]