numpy.invert¶

numpy.
invert
(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'invert'>¶ Compute bitwise inversion, or bitwise NOT, elementwise.
Computes the bitwise 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’scomplement 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 Nbit two’scomplement system can represent every integer in the range to .
Parameters:  x : array_like
Only integer and boolean types are handled.
 out : ndarray, 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 freshlyallocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
 where : array_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 keywordonly arguments, see the ufunc docs.
Returns:  out : ndarray or scalar
Result. This is a scalar if x is a scalar.
See also
bitwise_and
,bitwise_or
,bitwise_xor
,logical_not
binary_repr
 Return the binary representation of the input number as a string.
Notes
bitwise_not
is an alias forinvert
:>>> 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 bitwise 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 bitwidth:
>>> 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])