Source code for mars.tensor.arithmetic.hypot

#!/usr/bin/env python
# -*- coding: utf-8 -*-
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#      http://www.apache.org/licenses/LICENSE-2.0
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import numpy as np

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


@arithmetic_operand(sparse_mode='binary_and')
class TensorHypot(TensorBinOp):
    _op_type_ = OperandDef.HYPOT
    _func_name = 'hypot'


@infer_dtype(np.hypot)
def hypot(x1, x2, out=None, where=None, **kwargs):
    """
    Given the "legs" of a right triangle, return its hypotenuse.

    Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise.  If `x1` or
    `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type),
    it is broadcast for use with each element of the other argument.
    (See Examples)

    Parameters
    ----------
    x1, x2 : array_like
        Leg of the triangle(s).
    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 array 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
    -------
    z : Tensor
        The hypotenuse of the triangle(s).

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

    >>> mt.hypot(3*mt.ones((3, 3)), 4*mt.ones((3, 3))).execute()
    array([[ 5.,  5.,  5.],
           [ 5.,  5.,  5.],
           [ 5.,  5.,  5.]])

    Example showing broadcast of scalar_like argument:

    >>> mt.hypot(3*mt.ones((3, 3)), [4]).execute()
    array([[ 5.,  5.,  5.],
           [ 5.,  5.,  5.],
           [ 5.,  5.,  5.]])
    """
    op = TensorHypot(**kwargs)
    return op(x1, x2, out=out, where=where)