Source code for mars.tensor.arithmetic.sqrt

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 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
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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


@arithmetic_operand(sparse_mode='unary')
class TensorSqrt(TensorUnaryOp):
    _op_type_ = OperandDef.SQRT
    _func_name = 'sqrt'


@infer_dtype(np.sqrt)
def sqrt(x, out=None, where=None, **kwargs):
    """
    Return the positive square-root of an tensor, element-wise.

    Parameters
    ----------
    x : array_like
        The values whose square-roots are required.
    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
        An tensor of the same shape as `x`, containing the positive
        square-root of each element in `x`.  If any element in `x` is
        complex, a complex tensor is returned (and the square-roots of
        negative reals are calculated).  If all of the elements in `x`
        are real, so is `y`, with negative elements returning ``nan``.
        If `out` was provided, `y` is a reference to it.

    Notes
    -----
    *sqrt* has--consistent with common convention--as its branch cut the
    real "interval" [`-inf`, 0), and is continuous from above on it.
    A branch cut is a curve in the complex plane across which a given
    complex function fails to be continuous.

    Examples
    --------
    >>> import mars.tensor as mt

    >>> mt.sqrt([1,4,9]).execute()
    array([ 1.,  2.,  3.])

    >>> mt.sqrt([4, -1, -3+4J]).execute()
    array([ 2.+0.j,  0.+1.j,  1.+2.j])

    >>> mt.sqrt([4, -1, mt.inf]).execute()
    array([  2.,  NaN,  Inf])
    """
    op = TensorSqrt(**kwargs)
    return op(x, out=out, where=where)