Create efl.py

efl.py

@@ -0,0 +1,57 @@
+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)
+            ]