numpy.log2¶

numpy.log2(x,/,out=None,*,where=True,casting='same_kind',order='K',dtype=None,subok=True[,signature,extobj]) = <ufunc 'log2'>¶

Base-2 logarithm ofx.

Parameters:

x:array_like Input values. out:ndarray, 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 orNone,a freshly-allocated array is returned. A tuple (possible only as akeyword argument) must have length equal to the number of outputs. where:array_like, optional Values of True indicate to calculate the ufunc at that position, valuesof False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs.

Returns:

y:ndarray Base-2 logarithm ofx.This is a scalar ifx is a scalar.

See also

log,log10,log1p,emath.log2

Notes

New in version 1.3.0.

Logarithm is a multivalued function: for eachx there is an infinitenumber ofz such that2**z = x. The convention is to return thezwhose imaginary part lies in[-pi, pi].

For real-valued input data types,log2 always returns real output.For each value that cannot be expressed as a real number or infinity,it yieldsnan and sets theinvalid floating point error flag.

For complex-valued input,log2 is a complex analytical function thathas a branch cut[-inf, 0] and is continuous from above on it.log2handles the floating-point negative zero as an infinitesimal negativenumber, conforming to the C99 standard.

Examples

>>>x=np.array([0,1,2,2**4])>>>np.log2(x)array([-Inf,   0.,   1.,   4.])

>>>xi=np.array([0+1.j,1,2+0.j,4.j])>>>np.log2(xi)array([ 0.+2.26618007j,  0.+0.j        ,  1.+0.j        ,  2.+2.26618007j])