将字节数组转换为 numpy 数组的最快方法

Question

I have a bytearray which I want to convert to a numpy array of int16 to perform FFT operations on. The bytearray is coming out of a UDP socket so first I convert two consecutive bytes into an int16 using struct.unpack , and then convert it a numpy array using np.asarray .

Current approach, however, is too slow. Original bytearray is of length 1e6 bytes, so each of the mentioned steps ( struct.unpack and np.asarray ) takes 20 ms and with a total of 40ms. This is a relatively long frame time for my applications so I need it a bit shortened.

Currently, I'm doing this:

temp1 = self.data_buffer[0:FRAME_LEN_B]
self.temp_list = np.asarray(struct.unpack('h' * (len(temp1) // 2), temp1))

Answer 1

You can try np.frombuffer . This can wrap any object supporting the buffer protocol , whichbytearray explicitly does, into an array:

arr = np.frombuffer(self.data_buffer, dtype=np.int16, size=FRAME_LEN_B // 2)

You can manipulate the array however you want after that: slice, reshape, transpose, etc.

If your native byte order is opposite to what you have coming in from the network, you can swap the interpretation order without having to swap the data in-place:

dt = np.dtype(np.int16)
dt.newbyteorder('>')
arr = np.frombuffer(self.data_buffer, dtype=dt, size=FRAME_LEN_B // 2)

If the order is non-native, operations on the array may take longer, as the data will have to be swapped every time on the fly. You can therefore change the byte order in-place ahead of time if that is the case:

arr.byteswap(inplace=True)

This will overwrite the contents of the original packet. If you want to make a separate copy, just set inplace=False , which is the default.