epmd.py

class EmergentProgrammer:
    def __init__(self):
        self.action_primitives = ['map', 'filter', 'reduce', 'transform', 'compose']
        self.discovered_actions = set()
        self.fractal_scales = {}  # scale_level: {actions, dependencies}
    
    def discover_from_conditions(self, conditions, parameters, scale_level=0):
        """Discover new programming actions from conditions and parameters"""
        discovered = []
        
        # Analyze parameter patterns for action discovery
        param_patterns = self._analyze_parameter_patterns(parameters)
        condition_structure = self._analyze_condition_structure(conditions)
        
        # Generate new actions based on patterns
        for pattern in param_patterns:
            new_action = self._synthesize_action(pattern, condition_structure)
            if new_action and self._validate_action(new_action, scale_level):
                discovered.append(new_action)
                self.discovered_actions.add(new_action)
        
        # Update fractal scale registry
        self._update_fractal_scale(scale_level, discovered, parameters)
        
        return discovered
    
    def _synthesize_action(self, pattern, conditions):
        """Synthesize new action from patterns and conditions"""
        # Pattern-based action generation
        if pattern.get('type') == 'iterative':
            return f"cascade_{pattern.get('domain')}"
        elif pattern.get('type') == 'transformative':
            return f"resonate_{conditions.get('context', 'general')}"
        elif pattern.get('type') == 'emergent':
            return f"fractalize_{pattern.get('complexity')}"
        
        return None
    
    def _validate_action(self, action, scale_level):
        """Validate action maintains fractal resonance"""
        # Check scale consistency (EFL Axiom)
        curvature = self._compute_action_curvature(action, scale_level)
        return abs(curvature) < 0.1  # Maintain flat emergence