#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2020 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 ..utils import infer_dtype from .core import TensorBinOp from .utils import arithmetic_operand @arithmetic_operand(sparse_mode='always_false') class TensorCopysign(TensorBinOp): _op_type_ = OperandDef.COPYSIGN _func_name = 'copysign' [docs]@infer_dtype(np.copysign) def copysign(x1, x2, out=None, where=None, **kwargs): """ Change the sign of x1 to that of x2, element-wise. If both arguments are arrays or sequences, they have to be of the same length. If `x2` is a scalar, its sign will be copied to all elements of `x1`. Parameters ---------- x1 : array_like Values to change the sign of. x2 : array_like The sign of `x2` is copied to `x1`. 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 values of `x1` with the sign of `x2`. Examples -------- >>> import mars.tensor as mt >>> mt.copysign(1.3, -1).execute() -1.3 >>> (1/mt.copysign(0, 1)).execute() inf >>> (1/mt.copysign(0, -1)).execute() -inf >>> mt.copysign([-1, 0, 1], -1.1).execute() array([-1., -0., -1.]) >>> mt.copysign([-1, 0, 1], mt.arange(3)-1).execute() array([-1., 0., 1.]) """ op = TensorCopysign(**kwargs) return op(x1, x2, out=out, where=where)