nums.numpy.subtract

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

Subtract arguments, element-wise.

This docstring was copied from numpy.subtract.

Some inconsistencies with the NumS version may exist.

Parameters

x1 (BlockArray) – The arrays to be subtracted from each other. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
x2 (BlockArray) – The arrays to be subtracted from each other. 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

y – The difference of x1 and x2, element-wise.

Return type

BlockArray

Notes

Equivalent to x1 - x2 in terms of array broadcasting.

Examples

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

>>> nps.subtract(nps.array(1.0), nps.array(4.0)).get()  
array(-3.)

>>> x1 = nps.arange(9.0).reshape((3, 3))  
>>> x2 = nps.arange(3.0)  
>>> nps.subtract(x1, x2).get()  
array([[ 0.,  0.,  0.],
       [ 3.,  3.,  3.],
       [ 6.,  6.,  6.]])

nums.numpy.right_shift

nums.numpy.true_divide