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)