nums.numpy.bitwise_xor

nums.numpy.bitwise_xor(x1, x2, out=None, where=True, **kwargs)[source]

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

This docstring was copied from numpy.bitwise_xor.

Some inconsistencies with the NumS version may exist.

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

Parameters

  • x1 (BlockArray) – 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).

  • x2 (BlockArray) – 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).

  • out (BlockArray, None, or 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.

  • where (BlockArray, 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.

  • **kwargs – For other keyword-only arguments, see the ufunc docs.

Returns

out – Result.

Return type

BlockArray

See also

logical_xor, bitwise_and, bitwise_or

binary_repr

Return the binary representation of the input number as a string.

Examples

The The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

number 13 is represented by 00001101. Likewise, 17 is represented by 00010001. The bit-wise XOR of 13 and 17 is therefore 00011100, or 28:

>>> nps.bitwise_xor(nps.array(13), nps.array(17)).get()  
nps.array(28)

>>> nps.bitwise_xor(nps.array([31,3]), nps.array([5,6])).get()  
array([26,  5])
>>> nps.bitwise_xor(nps.array([True, True]), nps.array([False, True])).get()  
array([ True, False])