Bitshuffle is an algorithm that rearranges typed, binary data for improving
compression, as well as a python/C package that implements this algorithm within
the Numpy framework.

The library can be used along side HDF5 to compress and decompress datasets and
is integrated through the dynamically loaded filters framework. Bitshuffle is
HDF5 filter number 32008.

Algorithmically, Bitshuffle is closely related to HDF5's Shuffle filter except
it operates at the bit level instead of the byte level. Arranging a typed data
array in to a matrix with the elements as the rows and the bits within the
elements as the columns, Bitshuffle "transposes" the matrix, such that all the
least-significant-bits are in a row, etc.

This does not in itself compress data, only rearranges it for more efficient
compression. To perform the actual compression you will need a compression
library. Bitshuffle has been designed to be well matched to Marc Lehmann's LZF
as well as LZ4 and ZSTD. Note that because Bitshuffle modifies the data at the
bit level, sophisticated entropy reducing compression libraries such as GZIP and
BZIP are unlikely to achieve significantly better compression than simpler and
faster duplicate-string-elimination algorithms such as LZF, LZ4 and ZSTD.
Bitshuffle thus includes routines (and HDF5 filter options) to apply LZ4 and
ZSTD compression to each block after shuffling.