jax.numpy.bitwise_not¶

jax.numpy.bitwise_not(x)¶

Compute bit-wise inversion, or bit-wise NOT, element-wise.

LAX-backend implementation of invert(). Original docstring below.

invert(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])

Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ~.

For signed integer inputs, the two’s complement is returned. In a two’s-complement system negative numbers are represented by the two’s complement of the absolute value. This is the most common method of representing signed integers on computers 1. A N-bit two’s-complement system can represent every integer in the range \(-2^{N-1}\) to \(+2^{N-1}-1\).

Parameters: x (array_like) – Only integer and boolean types are handled.
Returns: out – Result. This is a scalar if x is a scalar.
Return type: ndarray or scalar

binary_repr(): Return the binary representation of the input number as a string.

Notes

bitwise_not is an alias for invert:

>>> np.bitwise_not is np.invert
True

References

1: Wikipedia, “Two’s complement”, https://en.wikipedia.org/wiki/Two’s_complement

Examples

We’ve seen that 13 is represented by 00001101. The invert or bit-wise NOT of 13 is then:

>>> x = np.invert(np.array(13, dtype=np.uint8))
>>> x
242
>>> np.binary_repr(x, width=8)
'11110010'

The result depends on the bit-width:

>>> x = np.invert(np.array(13, dtype=np.uint16))
>>> x
65522
>>> np.binary_repr(x, width=16)
'1111111111110010'

When using signed integer types the result is the two’s complement of the result for the unsigned type:

>>> np.invert(np.array([13], dtype=np.int8))
array([-14], dtype=int8)
>>> np.binary_repr(-14, width=8)
'11110010'

Booleans are accepted as well:

>>> np.invert(np.array([True, False]))
array([False,  True])