bundle

# -*- coding: utf-8 -*-

"""
Mycelium-Inspired Compiler Framework

This code provides a basic framework for a compiler inspired by mycelium networks,
swarm intelligence, and other bio-inspired concepts.

Author: j.mosij 
License: MIT License

Sources:
    - https://docs.python.org/3/library/networkx.html
    - https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.swarm.html
    - ... (Add any other relevant sources here)
"""

import networkx as nx
import random
from scipy.optimize import swarm

class CodeModule:
    def __init__(self, name, function):
        self.name = name
        self.function = function
        self.inputs = []  # List of modules this module receives input from
        self.outputs = []  # List of modules this module sends output to

    def run(self, data):
        """
        Executes the module's function on the provided data.
        May interact with other modules through messaging (optional).
        """
        return self.function(data)

class ParserModule(CodeModule):
    def __init__(self):
        super().__init__("Parser", self.parse_code)

    def parse_code(self, code_input):
        # Implement parsing logic here (e.g., using a parser generator like ANTLR)
        # This example uses a simplified placeholder
        print(f"ParserModule: Parsing code: {code_input}")
        return {"tokens": ["token1", "token2", "token3"]} 

class OptimizerModule(CodeModule):
    def __init__(self):
        super().__init__("Optimizer", self.optimize_code)

    def optimize_code(self, parsed_code):
        # Implement optimization techniques (e.g., constant folding, dead code elimination)
        # This example uses a simplified placeholder
        print(f"OptimizerModule: Optimizing code: {parsed_code}")
        # (Simplified swarm optimization example)
        def fitness_function(parameters):
            # Evaluate the fitness of the optimized code based on metrics like 
            # execution time, memory usage, or readability
            # ...
            return sum(parameters)  # Example: Minimize the sum of parameters

        bounds = [(0, 1)] * 5  # Example bounds
        best_parameters, best_fitness = swarm(fitness_function, bounds, swarmsize=10, iters=100)
        print(f"OptimizerModule: Best parameters: {best_parameters}")
        return {"optimized_tokens": ["opt_token1", "opt_token2"]} 

class GeneratorModule(CodeModule):
    def __init__(self, target_language):
        super().__init__("Generator", self.generate_code)
        self.target_language = target_language

    def generate_code(self, optimized_code):
        # Generate code in the target language based on the optimized representation
        # This example uses a simplified placeholder
        print(f"GeneratorModule: Generating code for {self.target_language}")
        generated_code = f"/* Generated Code in {self.target_language} */\n"
        for token in optimized_code.get("optimized_tokens", []):
            generated_code += f"{token} "
        return generated_code

class CodeCompiler:
    def __init__(self):
        self.graph = nx.DiGraph()  # Use a directed graph for better representation of data flow
        self.modules = {}

    def add_module(self, module):
        self.graph.add_node(module.name)
        self.modules[module.name] = module

    def connect_modules(self, source_module, target_module):
        self.graph.add_edge(source_module.name, target_module.name)
        source_module.outputs.append(target_module)
        target_module.inputs.append(source_module)

    def compile(self, code_input, target_language):
        # Instantiate modules
        parser = ParserModule()
        optimizer = OptimizerModule()
        generator = GeneratorModule(target_language)

        # Connect modules (example: pipeline with optional feedback loop)
        self.add_module(parser)
        self.add_module(optimizer)
        self.add_module(generator)
        self.connect_modules(parser, optimizer)
        self.connect_modules(optimizer, generator)
        # Optional: Add a feedback loop from generator to optimizer 
        # self.connect_modules(generator, optimizer) 

        # Run modules sequentially
        parsed_code = parser.run(code_input)
        optimized_code = optimizer.run(parsed_code)
        generated_code = generator.run(optimized_code)

        return generated_code

# Example Usage
user_code = "print('Hello, world!')"
target_language = "Python"

compiler = CodeCompiler()
generated_code = compiler.compile(user_code, target_language)

print(generated_code) 

# Further Enhancements (to be implemented):
# 1. Implement basic messaging between modules (e.g., using queues or shared memory).
# 2. Explore different network topologies (e.g., ring, star, fully connected).
# 3. Implement more sophisticated optimization algorithms (e.g., genetic algorithms, simulated annealing).
# 4. Integrate cellular automata for initial code structure generation.
# 5. Integrate a language model for code generation and user intent understanding.
# 6. Implement a visualization system for the compiler network and optimization process.
# 7. Thoroughly test and evaluate the compiler's performance and code quality.