[Numpy-discussion] converting list of int16 values to bitmask and back to bitmask and back to list of int32\float values

[Thomas Jollans]

On 08/10/17 09:12, Nissim Derdiger wrote:
> Hi again,
> I realize that my question was not clear enough, so I've refined it into one runnable function (attached below)
> My question is basically - is there a way to perform the same operation, but faster using NumPy (or even just by using Python better..)
> Thanks again and sorry for the unclearness..
> Nissim.
>
> import struct
>
> def Convert():
>     Endian = '<I' # Big endian
< is little endian. Make sure you're getting out the right values!
>     ParameterFormat = 'f' # float32
>     RawDataList = [17252, 26334, 16141, 58057,17252, 15478, 16144, 43257] # list of int32 registers
>     NumOfParametersInRawData = int(len(RawDataList)/2)
>     Result = []
>     for i in range(NumOfParametersInRawData):
Iterating over indices is not very Pythonic, and there's usually a
better way. In this case: for int1, int2 in zip(RawDataList[::2],
RawDataList[1::2])

>         # pack every 2 registers, take only the first 2 bytes from each one, change their endianess than unpack them back to the Parameter format
>         Result.append((struct.unpack(ParameterFormat,(struct.pack(Endian,RawDataList[(i*2)+1])[0:2] + struct.pack('<I',RawDataList[i*2])[0:2])))[0])

You can do this a little more elegantly (and probably faster) with
struct by putting it in a list comprehension:

[struct.unpack('f', struct.pack('<HH', i1 & 0xffff, i2 & 0xffff))[0] for
i1, i2 in zip(raw_data[::2], raw_data[1::2])]

Numpy can also do it. You can get your array of little-endian shorts with


le_shorts = np.array(raw_data, dtype='<u2')

and then reinterpret the bytes backing it as float32 with np.frombuffer:

np.frombuffer(le_shorts.data, dtype='f4')

For small lists like the one in your example, the two approaches are
equally fast. For long ones, numpy is much faster:

In [82]: raw_data Out[82]: [17252, 26334, 16141, 58057, 17252, 15478,
16144, 43257] In [83]: raw_data2 = np.random.randint(0, 2**32,
size=10**6, dtype='u4') # 1 million random integers In [84]: %timeit
np.frombuffer(np.array(raw_data, dtype='<u2').data, dtype='f4') 6.45 µs
± 60.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) In
[85]: %timeit np.frombuffer(np.array(raw_data2, dtype='<u2').data,
dtype='f4') 854 µs ± 37.3 µs per loop (mean ± std. dev. of 7 runs, 1000
loops each) In [86]: %timeit [struct.unpack('f', struct.pack('<HH', i1 &
0xffff, i2 & 0xffff))[0] for i1, i2 in zip(raw_data[::2],
raw_data[1::2])] 4.87 µs ± 17.3 ns per loop (mean ± std. dev. of 7 runs,
100000 loops each) In [87]: %timeit [struct.unpack('f',
struct.pack('<HH', i1 & 0xffff, i2 & 0xffff))[0] for i1, i2 in
zip(raw_data2[::2], raw_data2[1::2])] 3.6 s ± 9.78 ms per loop (mean ±
std. dev. of 7 runs, 1 loop each)

-- Thomas