float_power(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Some inconsistencies with the Dask version may exist.

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

Raise each base in :None:None:`x1` to the positionally-corresponding power in :None:None:`x2`. :None:None:`x1` and :None:None:`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 :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`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 <ufuncs.kwargs> .

Returns

y : ndarray

The bases in :None:None:`x1` raised to the exponents in :None:None:`x2`. This is a scalar if both :None:None:`x1` and :None:None:`x2` are scalars.

This docstring was copied from numpy.float_power.

See Also

power

power function that preserves type

Examples

Cube each element in a list.

This example is valid syntax, but we were not able to check execution

>>> x1 = range(6)  # doctest: +SKIP
... x1  # doctest: +SKIP
[0, 1, 2, 3, 4, 5]

This example is valid syntax, but we were not able to check execution

>>> np.float_power(x1, 3)  # doctest: +SKIP
array([   0.,    1.,    8.,   27.,   64.,  125.])

Raise the bases to different exponents.

This example is valid syntax, but we were not able to check execution

>>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]  # doctest: +SKIP
... np.float_power(x1, x2)  # doctest: +SKIP
array([  0.,   1.,   8.,  27.,  16.,   5.])

The effect of broadcasting.

This example is valid syntax, but we were not able to check execution

>>> x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])  # doctest: +SKIP
... x2  # doctest: +SKIP
array([[1, 2, 3, 3, 2, 1],
       [1, 2, 3, 3, 2, 1]])

This example is valid syntax, but we were not able to check execution

>>> np.float_power(x1, x2)  # doctest: +SKIP
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).

This example is valid syntax, but we were not able to check execution

>>> x3 = np.array([-1, -4])  # doctest: +SKIP
... with np.errstate(invalid='ignore'):  # doctest: +SKIP
...     p = np.float_power(x3, 1.5)
...

This example is valid syntax, but we were not able to check execution

>>> p  # doctest: +SKIP
array([nan, nan])

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

This example is valid syntax, but we were not able to check execution

>>> np.float_power(x3, 1.5, dtype=complex)  # doctest: +SKIP
array([-1.83697020e-16-1.j, -1.46957616e-15-8.j])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.ufunc.power

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