Source code for mars.tensor.arithmetic.modf

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np

from ... import opcodes as OperandDef
from ..array_utils import as_same_device, device
from ..datasource import tensor as astensor
from .core import TensorOutBinOp

class TensorModf(TensorOutBinOp):
    _op_type_ = OperandDef.MODF

    def __init__(self, casting="same_kind", dtype=None, sparse=False, **kw):
        super().__init__(_casting=casting, dtype=dtype, sparse=sparse, **kw)

    def _fun(self):
        return np.modf

    def execute(cls, ctx, op):
        inputs, device_id, xp = as_same_device(
            [ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True

        with device(device_id):
            kw = {"casting": op.casting}

            inputs_iter = iter(inputs)
            input = next(inputs_iter)
            if op.out1 is not None:
                out1 = next(inputs_iter)
                out1 = None
            if op.out2 is not None:
                out2 = next(inputs_iter)
                out2 = None
            if op.where is not None:
                where = kw["where"] = next(inputs_iter)
                where = None
            kw["order"] = op.order

                args = [input]
                if out1 is not None:
                if out2 is not None:
                y1, y2 = xp.modf(*args, **kw)
            except TypeError:
                if where is None:
                y1, y2 = xp.modf(input)
                y1, y2 = xp.where(where, y1, out1), xp.where(where, y2, out2)

            for c, res in zip(op.outputs, (y1, y2)):
                ctx[c.key] = res

[docs]def modf(x, out1=None, out2=None, out=None, where=None, **kwargs): """ Return the fractional and integral parts of a tensor, element-wise. The fractional and integral parts are negative if the given number is negative. Parameters ---------- x : array_like Input tensor. 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 ------- y1 : Tensor Fractional part of `x`. y2 : Tensor Integral part of `x`. Notes ----- For integer input the return values are floats. See Also -------- divmod : ``divmod(x, 1)`` is equivalent to ``modf`` with the return values switched, except it always has a positive remainder. Examples -------- >>> import mars.tensor as mt >>> mt.modf([0, 3.5]).execute() (array([ 0. , 0.5]), array([ 0., 3.])) >>> mt.modf(-0.5).execute() (-0.5, -0) """ x = astensor(x) op = TensorModf(**kwargs) return op(x, out1=out1, out2=out2, out=out, where=where)