NumPy数组“打乱”维度不匹配

Question

我算是个Python新手，正在做一个音频实验，灵感来自于这个进化的蒙娜丽莎。

下面的代码主要是想做以下几件事：

把一个指定的.wav文件读入到一个NumPy数组中。
检测波形中的“零交叉”，也就是数组元素符号变化的地方。在这些地方把数组分割成一个嵌套列表，里面是波形的“块”。
把正的块和负的块分开，然后把这些块打乱顺序，再交替组合成一个新的NumPy数组。因为列表里有超过2000个元素，所以我不能用random.shuffle()。
比较打乱后的数组和原始样本的“适应度”，适应度的定义是打乱数组和原始样本之间差值的平方。

最终，我会加入复制、变异和选择的过程，但现在我的适应度函数有问题。分割、打乱和重新组合后的数组和原始输入的维度不一样，导致了以下错误：

$ ValueError: operands could not be broadcast together with shapes (1273382) (1138213)

每次运行程序时，第二个数组的维度都不一样，但总是大约在1138000到1145000之间。我觉得在分割、打乱和重新组合的过程中丢失了一些块，我怀疑在第三步的列表推导式使用得不太对，但我就是搞不清楚哪里出了问题，为什么会这样。到底哪里出错了呢？

# Import scipy audio tools, numpy, and randomization tools
import scipy
from scipy.io import wavfile

import numpy

from random import shuffle, randint

# Read a wav file data array, detect zero crossings, split at zero crossings, and return a nested list.
def process_wav(input):

    # Assign the wavefile data array to a variable.
    wavdata = input[1]

    # Detect zero crossings, i.e. changes in sign in the waveform data. The line below returns an array of the indices of elements after which a zero crossing occurs.
    zerocrossings = numpy.where(numpy.diff(numpy.sign(wavdata)))[0]
    # Increment each element in the array by one. Otherwise, the indices are off.
    zerocrossings = numpy.add(numpy.ones(zerocrossings.size, zerocrossings.dtype), zerocrossings)

    wavdatalist = wavdata.tolist()
    zerocrossingslist = zerocrossings.tolist()

    # Split the list at zero crossings. The function below splits a list at the given indices.      
    def partition(alist, indices):
        return [alist[i:j] for i, j in zip([0]+indices, indices+[None])]

    return partition(wavdatalist, zerocrossingslist)


# Accept a list as input, separate into positive and negative chunks, shuffle, and return a shuffled nested list
def shuffle_wav(list):

    # Separate waveform chunks into positive and negative lists.
    positivechunks = []
    negativechunks = []

    for chunk in list:
        if chunk[0] < 0:
            negativechunks.append(chunk)
        elif chunk[0] > 0:
            positivechunks.append(chunk)
        elif chunk[0] == 0:
            positivechunks.append(chunk)

    # Shuffle the chunks and append them to a list, alternating positive with negative.
    shuffledchunks = []
    while len(positivechunks) >= 0 and len(negativechunks) > 0:
        currentpositivechunk = positivechunks.pop(randint(0, len(positivechunks)-1))
        shuffledchunks.append(currentpositivechunk)
        currentnegativechunk = negativechunks.pop(randint(0, len(negativechunks)-1))
        shuffledchunks.append(currentnegativechunk)

    return [chunk for sublist in shuffledchunks for chunk in sublist]

def get_fitness(array, target):
    return numpy.square(numpy.subtract(target, array))

# Read a sample wav file. The wavfile function returns a tuple of the file's sample rate and data as a numpy array, to be passed to the process_wav() function.
input = scipy.io.wavfile.read('sample.wav')     

wavchunks = process_wav(input)  
shuffledlist = shuffle_wav(wavchunks)   
output = numpy.array(shuffledlist, dtype='int16')
print get_fitness(output, input[1])

scipy.io.wavfile.write('output.wav', 44100, output)

编辑：这是完整的错误追踪信息：

Traceback (most recent call last):
  File "evowav.py", line 64, in <module>
    print get_fitness(output, input[1])
  File "evowav.py", line 56, in get_fitness
    return numpy.square(numpy.subtract(target, array))
ValueError: operands could not be broadcast together with shapes (1273382) (1136678)`

列表推导式 numpy 音频处理数组分割适应度函数零交叉检测数据打乱变异选择

NumPy数组“打乱”维度不匹配

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