mars.tensor.bitwise_and#

mars.tensor.bitwise_and(x1, x2, out=None, where=None, **kwargs)#

Compute the bit-wise AND of two tensors element-wise.

Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator &.

Parameters

  • x1 (array_like) – Only integer and boolean types are handled.

  • x2 (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 – Result.

Return type

array_like

See also

logical_and, bitwise_or, bitwise_xor

Examples

The number 13 is represented by 00001101. Likewise, 17 is represented by 00010001. The bit-wise AND of 13 and 17 is therefore 000000001, or 1:

>>> import mars.tensor as mt

>>> mt.bitwise_and(13, 17).execute()
1

>>> mt.bitwise_and(14, 13).execute()
12
>>> mt.bitwise_and([14,3], 13).execute()
array([12,  1])

>>> mt.bitwise_and([11,7], [4,25]).execute()
array([0, 1])
>>> mt.bitwise_and(mt.array([2,5,255]), mt.array([3,14,16])).execute()
array([ 2,  4, 16])
>>> mt.bitwise_and([True, True], [False, True]).execute()
array([False,  True])