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我有一个bytearray of 2^18 (262144) 字节。目标是将其转换为复杂值的numpy数组。字节数组中的包装是：

[0:1] Imaginary_0 16bit
[2:3] Real_0 16bit
[4:5] Imaginary_1 16bit
[6:7] Real_1 16bit
........
[262140:262141] Imaginary_65535 16bit
[262142:262143] Real_65535 16bit

我尝试了两种不同的方法，但执行时间仍然很长。有更快的方法吗？

def byte_array_to_complex_loop(byte_array, sample_count):
    y_iq = np.empty([sample_count], dtype=np.complex)
    for i in range(0, sample_count):
        read_data_bytes = byte_array[i*4: i*4+4]
        unpacked_data = struct.unpack("<hh", read_data_bytes)  # unpack into two singed short(s)
        y_iq[i] = complex(unpacked_data[1], unpacked_data[0])
    return y_iq


def byte_array_to_complex_np_iter(byte_array, sample_count):
    np_dtype = np.dtype([('i', '<i2'), ('q', '<i2')])
    np_read_data = np.frombuffer(byte_array, dtype=np_dtype)
    y_iq = [complex(item[1], item[0]) for item in np_read_data]
    return y_iq


def main():
    function_timing.clear()
    sample_count = 0x10000
    bytes_per_sample = 4
    function_timing.add_time("START")
    read_data = bytearray(os.urandom(sample_count*bytes_per_sample))
    function_timing.add_time("GENERATE RANDON BYTE ARRAY")
    y_iq1 = byte_array_to_complex_np_iter(read_data, sample_count)
    function_timing.add_time("Y_IQ CONV. (LIST COMPR)")
    y_iq2 = byte_array_to_complex_loop(read_data, sample_count)
    function_timing.add_time("Y_IQ CONV. (LOOP)")
    function_timing.print_times()

定时输出显示0.8105 and 0.5588每个功能的秒数。

- START:                       Delta:0.0000, Tot:0.0000
- GENERATE RANDON BYTE ARRAY:  Delta:0.0030, Tot:0.0030
- Y_IQ CONV. (LIST COMPR):     Delta:0.8105, Tot:0.8135
- Y_IQ CONV. (LOOP):           Delta:0.5588, Tot:1.3723
<<< END >>>
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1 回答 1

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一次解压整个字节数组，然后解释数据可能会更快。unpack您可以在格式字符串中添加成员数：

unpacked = np.array(struct.unpack("{0}<h".format(2*sample_count), byte_array)).reshape(sample_count,2)
return unpacked[1::2] + 1j*unpacked[::2] #2nd int16 is real part, 1st is imag part, in each pair of int16s
于 2019-12-10T16:30:11.293 回答