# Source code for mars.tensor.arithmetic.spacing

#!/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.
# 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="always_false")
class TensorSpacing(TensorUnaryOp):
_op_type_ = OperandDef.SPACING
_func_name = "spacing"
[docs]@infer_dtype(np.spacing)
def spacing(x, out=None, where=None, **kwargs):
"""
Return the distance between x and the nearest adjacent number.
Parameters
----------
x : array_like
Values to find the spacing of.
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
-------
out : array_like
The spacing of values of `x1`.
Notes
-----
It can be considered as a generalization of EPS:
``spacing(mt.float64(1)) == mt.finfo(mt.float64).eps``, and there
should not be any representable number between ``x + spacing(x)`` and
x for any finite x.
Spacing of +- inf and NaN is NaN.
Examples
--------
>>> import mars.tensor as mt
>>> (mt.spacing(1) == mt.finfo(mt.float64).eps).execute()
True
"""
op = TensorSpacing(**kwargs)
return op(x, out=out, where=where)