Source code for xorbits._mars.tensor.arithmetic.invert
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import numpy as np
from ... import opcodes as OperandDef
from ..utils import infer_dtype
from .core import TensorUnaryOp
from .utils import arithmetic_operand
@arithmetic_operand(sparse_mode="unary")
class TensorInvert(TensorUnaryOp):
_op_type_ = OperandDef.INVERT
_func_name = "invert"
[docs]@infer_dtype(np.invert)
def invert(x, out=None, where=None, **kwargs):
"""
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
:math:`-2^{N-1}` to :math:`+2^{N-1}-1`.
Parameters
----------
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
Returns
-------
out : array_like
Result.
See Also
--------
bitwise_and, bitwise_or, bitwise_xor
logical_not
Notes
-----
`bitwise_not` is an alias for `invert`:
>>> import mars.tensor as mt
>>> mt.bitwise_not is mt.invert
True
References
----------
.. [1] 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:
>>> 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])
"""
op = TensorInvert(**kwargs)
return op(x, out=out, where=where)