# mars.tensor.log2¶

mars.tensor.log2(x, out=None, where=None, **kwargs)[source]

Base-2 logarithm of x.

x : array_like
Input values.
out : Tensor, None, or tuple of tensor 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 tensor is returned. A tuple (possible only as a keyword 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, values of False indicate to leave the value in the output alone.

**kwargs

y : Tensor
Base-2 logarithm of x.

log, log10, log1p

Logarithm is a multivalued function: for each x there is an infinite number of z such that 2**z = x. The convention is to return the z whose 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 yields nan and sets the invalid floating point error flag.

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

>>> import mars.tensor as mt

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

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