Source code for mars.tensor.arithmetic.frexp

#!/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 device, as_same_device
from .core import TensorOutBinOp

class TensorFrexp(TensorOutBinOp):
    _op_type_ = OperandDef.FREXP
    _func_name = "frexp"

    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.frexp

    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

            # The out1 out2 are immutable because they are got from
            # the shared memory.
            mantissa, exponent = xp.frexp(input)
            if where is not None:
                mantissa, exponent = (
                    xp.where(where, mantissa, out1),
                    xp.where(where, exponent, out2),

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

[docs]def frexp(x, out1=None, out2=None, out=None, where=None, **kwargs): """ Decompose the elements of x into mantissa and twos exponent. Returns (`mantissa`, `exponent`), where `x = mantissa * 2**exponent``. The mantissa is lies in the open interval(-1, 1), while the twos exponent is a signed integer. Parameters ---------- x : array_like Tensor of numbers to be decomposed. out1 : Tensor, optional Output tensor for the mantissa. Must have the same shape as `x`. out2 : Tensor, optional Output tensor for the exponent. Must have the same shape as `x`. 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 ------- (mantissa, exponent) : tuple of tensors, (float, int) `mantissa` is a float array with values between -1 and 1. `exponent` is an int array which represents the exponent of 2. See Also -------- ldexp : Compute ``y = x1 * 2**x2``, the inverse of `frexp`. Notes ----- Complex dtypes are not supported, they will raise a TypeError. Examples -------- >>> import mars.tensor as mt >>> x = mt.arange(9) >>> y1, y2 = mt.frexp(x) >>> y1_result, y2_result = mt.ExecutableTuple([y1, y2]).execute() >>> y1_result array([ 0. , 0.5 , 0.5 , 0.75 , 0.5 , 0.625, 0.75 , 0.875, 0.5 ]) >>> y2_result array([0, 1, 2, 2, 3, 3, 3, 3, 4]) >>> (y1 * 2**y2).execute() array([ 0., 1., 2., 3., 4., 5., 6., 7., 8.]) """ op = TensorFrexp(**kwargs) return op(x, out1=out1, out2=out2, out=out, where=where)