nlcpy.power

nlcpy.power = <ufunc 'nlcpy_power'>

Computes the element-wise exponentiation of the inputs.

Parameters

x1, x2array_like

x1 is a base array and x2 is an exponent array. 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.

**kwargs

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

Returns

yndarray

The bases in x1 raised to the exponents in x2. If x1 and x2 are both scalars, this function returns the result as a 0-dimension ndarray.

Examples

Cube each element in a list.

>>> import nlcpy as vp
>>> x1 = vp.arange(6)
>>> x1
array([0, 1, 2, 3, 4, 5])
>>> vp.power(x1, 3)
array([  0,   1,   8,  27,  64, 125])

Raise the bases to different exponents.

>>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]
>>> vp.power(x1,x2)
array([ 0.,  1.,  8., 27., 16.,  5.])

The effect of broadcasting.

>>> x2 = vp.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
>>> x2
array([[1, 2, 3, 3, 2, 1],
       [1, 2, 3, 3, 2, 1]])
>>> vp.power(x1,x2)
array([[ 0,  1,  8, 27, 16,  5],
       [ 0,  1,  8, 27, 16,  5]])