nums.numpy.heaviside

nums.numpy.heaviside(x1, x2, out=None, where=True, **kwargs)

Compute the Heaviside step function.

This docstring was copied from numpy.heaviside.

Some inconsistencies with the NumS version may exist.

The Heaviside step function is defined as:

                      0   if x1 < 0
heaviside(x1, x2) =  x2   if x1 == 0
                      1   if x1 > 0

where x2 is often taken to be 0.5, but 0 and 1 are also sometimes used.

Parameters

• x1 (BlockArray) – Input values.

• x2 (BlockArray) – The value of the function when x1 is 0. 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 – The output array, element-wise Heaviside step function of x1.

Return type

BlockArray

References

Examples

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

>>> nps.heaviside(nps.array([-1.5, 0, 2.0]), nps.array(0.5)).get()  
array([ 0. ,  0.5,  1. ])
>>> nps.heaviside(nps.array([-1.5, 0, 2.0]), nps.array(1)).get()  
array([ 0.,  1.,  1.])