On Wed, Jul 21, 2021 at 2:40 PM Neal Becker <[email protected]> wrote:
> In my application I need to pack bits of a specified group size into
> integral values.
> Currently np.packbits only packs into full bytes.
> For example, I might have a string of bits encoded as a np.uint8
> vector with each uint8 item specifying a single bit 1/0. I want to
> encode them 4 bits at a time into a np.uint32 vector.
>
> python code to implement this:
>
> ---------------
> def pack_bits (inp, bits_per_word, dir=1, dtype=np.int32):
> assert bits_per_word <= np.dtype(dtype).itemsize * 8
> assert len(inp) % bits_per_word == 0
> out = np.empty (len (inp)//bits_per_word, dtype=dtype)
> i = 0
> o = 0
> while i < len(inp):
> ret = 0
> for b in range (bits_per_word):
> if dir > 0:
> ret |= inp[i] << b
> else:
> ret |= inp[i] << (bits_per_word - b - 1)
> i += 1
> out[o] = ret
> o += 1
> return out
> ---------------
>
Can't you just `packbits` into a uint8 array and then convert that to
uint32? If I change `dtype` in your code from `np.int32` to `np.uint32` (as
you mentioned in your email) I can do this:
rng = np.random.default_rng()
arr = (rng.uniform(size=32) < 0.5).astype(np.uint8)
group_size = 4
original = pack_bits(arr, group_size, dtype=np.uint32)
new = np.packbits(arr.reshape(-1, group_size), axis=-1,
bitorder='little').ravel().astype(np.uint32)
print(np.array_equal(new, original))
# True
There could be edge cases where the result dtype is too small, but I
haven't thought about that part of the problem. I assume this would work as
long as `group_size <= 8`.
András
> It looks like unpackbits has a "count" parameter but packbits does not.
> Also would be good to be able to specify an output dtype.
> _______________________________________________
> NumPy-Discussion mailing list
> [email protected]
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
_______________________________________________
NumPy-Discussion mailing list
[email protected]
https://mail.python.org/mailman/listinfo/numpy-discussion
