mars.tensor.invert(x, out=None, where=None, **kwargs)[source]#

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 tensors. 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 \(+2^{N-1}-1\).

  • x (array_like) – Only integer and boolean types are handled.

  • out (Tensor, None, or tuple of Tensor 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 tensor is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

  • where (array_like, optional) – Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.

  • **kwargs


out – Result.

Return type



bitwise_not is an alias for invert:

>>> import mars.tensor as mt
>>> mt.bitwise_not is mt.invert



Wikipedia, “Two’s complement”,’s_complement


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

>>> mt.invert(mt.array([13], dtype=mt.uint8)).execute()
array([242], dtype=uint8)

The result depends on the bit-width:

>>> mt.invert(mt.array([13], dtype=mt.uint16)).execute()
array([65522], dtype=uint16)

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

>>> mt.invert(mt.array([13], dtype=mt.int8)).execute()
array([-14], dtype=int8)

Booleans are accepted as well:

>>> mt.invert(mt.array([True, False])).execute()
array([False,  True])