# Source code for mars.tensor.arithmetic.trunc

#!/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="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`.
See Also
--------
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)