nlcpy.bitwise_and = <ufunc 'nlcpy_bitwise_and'>

Computes the bit-wise AND of two arrays element-wise.

This ufunc implements the C/Python operator &.

x1, x2array_like

Only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

outndarray or 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 array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

wherearray_like, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.


For other keyword-only arguments, see the section Optional Keyword Arguments.


y = x1 & x2. If x1 and x2 are both scalars, this function returns the result as a 0-dimension ndarray.



Computes the logical AND of two arrays element-wise.


Computes the bit-wise OR of two arrays element-wise.


Computes the bit-wise XOR of two arrays element-wise.


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 nlcpy as vp
>>> vp.bitwise_and(13, 17)
>>> vp.bitwise_and(14, 13)
>>> vp.bitwise_and([14,3], 13)
array([12,  1])
>>> vp.bitwise_and([11,7], [4,25])
array([0, 1])
>>> vp.bitwise_and(vp.array([2,5,255]), vp.array([3,14,16]))
array([ 2,  4, 16])
>>> vp.bitwise_and([True, True], [False, True])
array([False,  True])