Compute bit-wise inversion, or bit-wise NOT, element-wise.
When calculating the bit-wise NOT of an element x, each element is first converted to its binary representation (which works just like the decimal system, only now we’re using 2 instead of 10):
where W is the bit-width of the type (i.e., 8 for a byte or uint8), and each is either 0 or 1. For example, 13 is represented as 00001101, which translates to .
The bit-wise operator is the result of
where is the NOT operator, which yields 1 whenever is 0 and yields 0 whenever is 1.
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 . A N-bit two’s-complement system can represent every integer in the range to .
x1 : ndarray
out : ndarray
bitwise_not is an alias for invert:
>>> np.bitwise_not is np.invert True
|||Wikipedia, “Two’s complement”, http://en.wikipedia.org/wiki/Two’s_complement|
We’ve seen that 13 is represented by 00001101. The invert or bit-wise NOT of 13 is then:
>>> np.invert(np.array(, dtype=uint8)) array(, 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(, dtype=uint16)) array(, 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(, 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)