numpy.float_power#

numpy.float_power(x1,x2,/,out=None,*,where=True,casting='same_kind',order='K',dtype=None,subok=True[,signature])=<ufunc'float_power'>#

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

Raise each base inx1 to the positionally-corresponding power inx2.x1 andx2 must be broadcastable to the same shape. This differs fromthe power function in that integers, float16, and float32 are promoted tofloats with a minimum precision of float64 so that the result is alwaysinexact. The intent is that the function will return a usable result fornegative powers and seldom overflow for positive powers.

Negative values raised to a non-integral value will returnnan.To get complex results, cast the input to complex, or specify thedtype to becomplex (see the example below).

Parameters:
x1array_like

The bases.

x2array_like

The exponents.Ifx1.shape!=x2.shape, they must be broadcastable to a commonshape (which becomes the shape of the output).

outndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must havea shape that the inputs broadcast to. If not provided or None,a freshly-allocated array is returned. A tuple (possible only as akeyword argument) must have length equal to the number of outputs.

wherearray_like, optional

This condition is broadcast over the input. At locations where thecondition is True, theout array will be set to the ufunc result.Elsewhere, theout array will retain its original value.Note that if an uninitializedout array is created via the defaultout=None, locations within it where the condition is False willremain uninitialized.

**kwargs

For other keyword-only arguments, see theufunc docs.

Returns:
yndarray

The bases inx1 raised to the exponents inx2.This is a scalar if bothx1 andx2 are scalars.

See also

power

power function that preserves type

Examples

>>>importnumpyasnp

Cube each element in a list.

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

The effect of broadcasting.

>>>x2=np.array([[1,2,3,3,2,1],[1,2,3,3,2,1]])>>>x2array([[1, 2, 3, 3, 2, 1],       [1, 2, 3, 3, 2, 1]])>>>np.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 innan(and a warning will be generated).

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

To get complex results, give the argumentdtype=complex.

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