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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import numpy as np

from ... import opcodes as OperandDef
from ..utils import infer_dtype
from .core import TensorUnaryOp
from .utils import arithmetic_operand

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)