On Wed, Jul 21, 2021 at 2:40 PM Neal Becker <ndbeck...@gmail.com> 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`. AndrĂ¡s > 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 > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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