jax.numpy.unpackbits#

jax.numpy.unpackbits(a,axis=None,count=None,bitorder='big')[source]#

Unpack the bits in a uint8 array.

JAX implementation ofnumpy.unpackbits().

Parameters:

• a (ArrayLike) – N-dimensional array of typeuint8.

• axis (int |None) – optional axis along which to unpack. If not specified,a willbe flattened

• count (int |None) – specify the number of bits to unpack (if positive) or the numberof bits to trim from the end (if negative).

• bitorder (str) –"big" (default) or"little": specify whether the bit orderis big-endian or little-endian.

Returns:

a uint8 array of unpacked bits.

Return type:

Array

See also

• jax.numpy.packbits(): this inverse ofunpackbits.

Examples

Unpacking bits from a scalar:

>>>jnp.unpackbits(jnp.uint8(27))# big-endian by defaultArray([0, 0, 0, 1, 1, 0, 1, 1], dtype=uint8)>>>jnp.unpackbits(jnp.uint8(27),bitorder="little")Array([1, 1, 0, 1, 1, 0, 0, 0], dtype=uint8)

Compare this to the Python binary representation:

>>>0b0001101127

Unpacking bits along an axis:

>>>vals=jnp.array([[154],...[49]],dtype='uint8')>>>bits=jnp.unpackbits(vals,axis=1)>>>bitsArray([[1, 0, 0, 1, 1, 0, 1, 0],       [0, 0, 1, 1, 0, 0, 0, 1]], dtype=uint8)

Usingpackbits() to invert this:

>>>jnp.packbits(bits,axis=1)Array([[154],       [ 49]], dtype=uint8)

Thecount keyword letsunpackbits serve as an inverse ofpackbitsin cases where not all bits are present:

>>>bits=jnp.array([1,1,0,1,1,0,1,1,0,1,1])# 11 bits>>>vals=jnp.packbits(bits)>>>valsArray([219,  96], dtype=uint8)>>>jnp.unpackbits(vals)# 16 zero-padded bitsArray([1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0], dtype=uint8)>>>jnp.unpackbits(vals,count=11)# specify 11 output bitsArray([1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], dtype=uint8)>>>jnp.unpackbits(vals,count=-5)# specify 5 bits to be trimmedArray([1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], dtype=uint8)