# Copyright 2022-2023 XProbe Inc.
# derived from 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 ...serialization.serializables import Int32Field
from ..array_utils import as_same_device, device
from ..datasource import tensor as astensor
from .core import TensorUnaryOp
from .utils import arithmetic_operand
@arithmetic_operand(init=False, sparse_mode="unary")
class TensorAround(TensorUnaryOp):
_op_type_ = OperandDef.AROUND
_decimals = Int32Field("decimals")
_func_name = "around"
@property
def decimals(self):
return self._decimals
def __init__(
self,
decimals=None,
casting="same_kind",
err=None,
dtype=None,
sparse=False,
**kw
):
err = err if err is not None else np.geterr()
super().__init__(
_decimals=decimals,
_casting=casting,
_err=err,
dtype=dtype,
sparse=sparse,
**kw
)
@property
def ufunc_extra_params(self):
return {"decimals": self._decimals}
@classmethod
def execute(cls, ctx, op):
(a,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
with device(device_id):
ctx[op.outputs[0].key] = xp.around(a, decimals=op.decimals)
[docs]def around(a, decimals=0, out=None):
"""
Evenly round to the given number of decimals.
Parameters
----------
a : array_like
Input data.
decimals : int, optional
Number of decimal places to round to (default: 0). If
decimals is negative, it specifies the number of positions to
the left of the decimal point.
out : Tensor, optional
Alternative output tensor in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary.
Returns
-------
rounded_array : Tensor
An tensor of the same type as `a`, containing the rounded values.
Unless `out` was specified, a new tensor is created. A reference to
the result is returned.
The real and imaginary parts of complex numbers are rounded
separately. The result of rounding a float is a float.
See Also
--------
Tensor.round : equivalent method
ceil, fix, floor, rint, trunc
Notes
-----
For values exactly halfway between rounded decimal values, NumPy
rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
-0.5 and 0.5 round to 0.0, etc. Results may also be surprising due
to the inexact representation of decimal fractions in the IEEE
floating point standard [1]_ and errors introduced when scaling
by powers of ten.
References
----------
.. [1] "Lecture Notes on the Status of IEEE 754", William Kahan,
http://www.cs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF
.. [2] "How Futile are Mindless Assessments of
Roundoff in Floating-Point Computation?", William Kahan,
http://www.cs.berkeley.edu/~wkahan/Mindless.pdf
Examples
--------
>>> import mars.tensor as mt
>>> mt.around([0.37, 1.64]).execute()
array([ 0., 2.])
>>> mt.around([0.37, 1.64], decimals=1).execute()
array([ 0.4, 1.6])
>>> mt.around([.5, 1.5, 2.5, 3.5, 4.5]).execute() # rounds to nearest even value
array([ 0., 2., 2., 4., 4.])
>>> mt.around([1,2,3,11], decimals=1).execute() # tensor of ints is returned
array([ 1, 2, 3, 11])
>>> mt.around([1,2,3,11], decimals=-1).execute()
array([ 0, 0, 0, 10])
"""
dtype = astensor(a).dtype
op = TensorAround(decimals=decimals, dtype=dtype)
return op(a, out=out)
round_ = around