请注意，您可以从一个复杂的dtype数组开始：
In [4]: data = np.zeros(250,dtype='float32, (250000,2)float32')
并将其视为同质dtype的数组：
In [5]: data2 = data.view('float32')然后将其转换回复杂的dtype：
In [7]: data3 = data2.view('float32, (250000,2)float32')更改dtype是一项非常快速的操作。它不会影响基础数据，只会影响NumPy解释数据的方式。因此，更改dtype实际上是没有成本的。
因此，您可以通过上述技巧将有关具有简单（均质）dtypes的数组的知识轻松应用于您的复杂dtypes。
下面的代码从JF Sebastian的答案中借鉴了许多想法。
import numpy as npimport multiprocessing as mpimport contextlibimport ctypesimport structimport base64def depre(arg): chunk, counter = arg print len(chunk), counter for x in chunk: peak_counter = 0 data_buff = base64.b64depre(x) buff_size = len(data_buff) / 4 unpack_format = ">%dL" % buff_size index = 0 for y in struct.unpack(unpack_format, data_buff): buff1 = struct.pack("I", y) buff2 = struct.unpack("f", buff1)[0] with shared_arr.get_lock(): data = tonumpyarray(shared_arr).view( [('f0', '<f4'), ('f1', '<f4', (250000, 2))]) if (index % 2 == 0): data[counter][1][peak_counter][0] = float(buff2) else: data[counter][1][peak_counter][1] = float(buff2) peak_counter += 1 index += 1 counter += 1def pool_init(shared_arr_): global shared_arr shared_arr = shared_arr_ # must be inherited, not passed as an argumentdef tonumpyarray(mp_arr): return np.frombuffer(mp_arr.get_obj())def numpy_array(shared_arr, peaks): """Fills the NumPy array 'data' with m/z-intensity values acquired from b64 decoding and unpacking the binary string read from the mzXML file, which is stored in the list 'peaks'. The m/z values are assumed to be ordered without validating this assumption. Note: This function uses multi-processing """ processors = mp.cpu_count() with contextlib.closing(mp.Pool(processes=processors, initializer=pool_init, initargs=(shared_arr, ))) as pool: chunk_size = int(len(peaks) / processors) map_parameters = [] for i in range(processors): counter = i * chunk_size # WARNING: I removed -1 from (i + 1)*chunk_size, since the right # index is non-inclusive. chunk = peaks[i*chunk_size : (i + 1)*chunk_size] map_parameters.append((chunk, counter)) pool.map(depre, map_parameters)if __name__ == '__main__': shared_arr = mp.Array(ctypes.c_float, (250000 * 2 * 250) + 250) peaks = ... numpy_array(shared_arr, peaks)如果您可以保证执行分配的各种过程
if (index % 2 == 0): data[counter][1][peak_counter][0] = float(buff2)else: data[counter][1][peak_counter][1] = float(buff2)
从不竞争更改同一位置的数据，那么我相信您实际上可以放弃使用锁
with shared_arr.get_lock():
但是我对您的代码不够了解，无法确定，因此为了安全起见，我添加了锁。
