log2(x, out=None, where=None, **kwargs)¶
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.
- 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
nanand 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])