# Source code for mars.tensor.arithmetic.log2

```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ..utils import infer_dtype
from .core import TensorUnaryOp
from .utils import arithmetic_operand
@arithmetic_operand(sparse_mode="unary")
class TensorLog2(TensorUnaryOp):
_op_type_ = OperandDef.LOG2
_func_name = "log2"
[docs]@infer_dtype(np.log2)
def log2(x, out=None, where=None, **kwargs):
"""
Base-2 logarithm of `x`.
Parameters
----------
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
Returns
-------
y : Tensor
Base-2 logarithm of `x`.
See Also
--------
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.
Examples
--------
>>> 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])
"""
op = TensorLog2(**kwargs)
return op(x, out=out, where=where)
```