# numpy.invert#

numpy.invert(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature]) = <ufunc 'invert'>#

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

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 bit-wise NOT of the absolute value is returned. In a two’s-complement system, this operation effectively flips all the bits, resulting in a representation that corresponds to the negative of the input plus one. 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:
xarray_like

Only integer and boolean types are handled.

outndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

wherearray_like, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

**kwargs

For other keyword-only arguments, see the ufunc docs.

Returns:
outndarray or scalar

Result. This is a scalar if x is a scalar.

Notes

numpy.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

>>> import numpy as np


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
np.uint8(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
np.uint16(65522)
>>> np.binary_repr(x, width=16)
'1111111111110010'


When using signed integer types, the result is the bit-wise NOT of the unsigned type, interpreted as a signed integer:

>>> 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])


The ~ operator can be used as a shorthand for np.invert on ndarrays.

>>> x1 = np.array([True, False])
>>> ~x1
array([False,  True])