numpy.invert()

numpy.invert(x[, out]) =

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 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 [R32]. A N-bit two?s-complement system can represent every integer in the range $-2^{N-1}$ to $+2^{N-1}-1$ .

Parameters:

Parameters:	x1 : array_like Only integer and boolean types are handled.
Returns:	out : array_like Result.

x1 : array_like

Only integer and boolean types are handled.

Returns:

out : array_like

Result.

Notes

bitwise_not is an alias for invert:

>>> np.bitwise_not is np.invert
True

References

[R32]

(1, 2) Wikipedia, ?Two?s complement?, http://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:

>>> np.invert(np.array([13], dtype=uint8))
array([242], dtype=uint8)
>>> np.binary_repr(x, width=8)
'00001101'
>>> np.binary_repr(242, width=8)
'11110010'

The result depends on the bit-width:

>>> np.invert(np.array([13], dtype=uint16))
array([65522], dtype=uint16)
>>> np.binary_repr(x, width=16)
'0000000000001101'
>>> np.binary_repr(65522, 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=int8))
array([-14], dtype=int8)
>>> np.binary_repr(-14, width=8)
'11110010'

Booleans are accepted as well:

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

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.invert.html

doc_NumPy

2025-01-10 15:47:30

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