# Source code for mars.tensor.arithmetic.trunc

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# 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
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

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 TensorTrunc(TensorUnaryOp):
_op_type_ = OperandDef.TRUNC
_func_name = 'trunc'

[docs]@infer_dtype(np.trunc)
def trunc(x, out=None, where=None, **kwargs):
"""
Return the truncated value of the input, element-wise.

The truncated value of the scalar x is the nearest integer i which
is closer to zero than x is. In short, the fractional part of the
signed number x is discarded.

Parameters
----------
x : array_like
Input data.
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 or scalar
The truncated value of each element in x.

--------
ceil, floor, rint

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

>>> a = mt.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
>>> mt.trunc(a).execute()
array([-1., -1., -0.,  0.,  1.,  1.,  2.])
"""
op = TensorTrunc(**kwargs)
return op(x, out=out, where=where)