给我一段基于遗传算法的编曲代码

以下是一个基于遗传算法的简单编曲代码示例：

import numpy as np
import scipy.signal as sig
import pyaudio

# Define the target melody
target_melody = np.array([60, 62, 64, 65, 67, 69, 71, 72])

# Define the population size and number of generations
pop_size = 50
num_gen = 100

# Define the range of possible note values (midi numbers)
note_range = (48, 84)

# Define the fitness function
def fitness(melody):
    # Calculate the mean squared error between the target melody and the given melody
    mse = np.mean((target_melody - melody)**2)
    # Return the fitness as the negative of the mse
    return -mse

# Define the mutation function
def mutation(individual):
    # Randomly select a note index to mutate
    mutate_idx = np.random.randint(len(individual))
    # Randomly select a new note value within the note range
    new_note = np.random.randint(note_range[0], note_range[1]+1)
    # Mutate the selected note
    individual[mutate_idx] = new_note
    return individual

# Define the crossover function
def crossover(parent1, parent2):
    # Randomly select a crossover point
    crossover_idx = np.random.randint(len(parent1))
    # Create the child by combining the first part of parent1 with the second part of parent2
    child = np.concatenate((parent1[:crossover_idx], parent2[crossover_idx:]))
    return child

# Initialize the population with random melodies
population = [np.random.randint(note_range[0], note_range[1]+1, size=len(target_melody)) for _ in range(pop_size)]

# Iterate through the generations
for g in range(num_gen):
    # Evaluate the fitness of each individual
    fitnesses = [fitness(melody) for melody in population]
    # Select the best individuals to be parents for the next generation
    parents_idx = np.argsort(fitnesses)[-2:]
    parents = [population[idx] for idx in parents_idx]
    # Create the next generation by crossover and mutation
    next_gen = [crossover(parents[0], parents[1]) for _ in range(pop_size-2)]
    next_gen = [mutation(individual) if np.random.rand() < 0.1 else individual for individual in next_gen]
    next_gen.extend(parents)
    population = next_gen

    # Play the best melody of the current generation
    best_idx = np.argsort(fitnesses)[-1]
    best_melody = population[best_idx]
    freqs = 2**(best_melody/12)*440
    dur = 0.5*np.ones(len(best_melody))
    sig_data = np.zeros(int(np.sum(dur)*44100))
    idx = 0
    for f, d in zip(freqs, dur):
        t = np.arange(0, d, 1/44100)
        note_data = np.sin(2*np.pi*f*t)
        sig_data[idx:idx+len(note_data)] = note_data
        idx += len(note_data)
    p = pyaudio.PyAudio()
    stream = p.open(format=pyaudio.paFloat32, channels=1, rate=44100, output=True)
    stream.write(sig_data.astype(np.float32).tobytes())
    stream.stop_stream()
    stream.close()
    p.terminate()

该代码使用遗传算法生成适合目标旋律的旋律。每代中，基于音符的MIDI编号，计算每个个体的适应性，选择最好的两个个体作为下一代的父代，使用交叉和变异操作生成下一代。同时，播放当前代中最好的旋律。