flopscope.

flopscope.numpy.float_power

fnp.float_power(*args, **kwargs)[flopscope source][numpy source]

First array elements raised to powers from second array, element-wise.

Adapted from NumPy docs np.float_power

Areacore
Typecounted
NumPy Refnp.float_power
Cost
16×numel(output)\text{numel}(\text{output})
Flopscope Context

Element-wise exponentiation in float64.

Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be broadcastable to the same shape. This differs from the power function in that integers, float16, and float32 are promoted to floats with a minimum precision of float64 so that the result is always inexact. The intent is that the function will return a usable result for negative powers and seldom overflow for positive powers.

Negative values raised to a non-integral value will return nan. To get complex results, cast the input to complex, or specify the dtype to be complex (see the example below).

Parameters

x1:array_like

The bases.

x2:array_like

The exponents. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

out:ndarray, None, or tuple of ndarray and 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.

where:array_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 ufunc docs.

Returns

y:ndarray

The bases in x1 raised to the exponents in x2. This is a scalar if both x1 and x2 are scalars.

See also

Examples

>>> import flopscope.numpy as fnp

Cube each element in a list.

>>> x1 = range(6)
>>> x1
[0, 1, 2, 3, 4, 5]
>>> flops.float_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]
>>> flops.float_power(x1, x2)
array([  0.,   1.,   8.,  27.,  16.,   5.])

The effect of broadcasting.

>>> x2 = flops.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]])
>>> flops.float_power(x1, x2)
array([[  0.,   1.,   8.,  27.,  16.,   5.],
       [  0.,   1.,   8.,  27.,  16.,   5.]])

Negative values raised to a non-integral value will result in nan (and a warning will be generated).

>>> x3 = flops.array([-1, -4])
>>> with flops.errstate(invalid='ignore'):
... p = flops.float_power(x3, 1.5)
...
>>> p
array([nan, nan])

To get complex results, give the argument dtype=complex.

>>> flops.float_power(x3, 1.5, dtype=complex)
array([-1.83697020e-16-1.j, -1.46957616e-15-8.j])