Source code for mars.tensor.arithmetic.ldexp

#!/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 ..utils import infer_dtype
from ..datasource import tensor as astensor
from .core import TensorBinOp
from .utils import arithmetic_operand

class TensorLdexp(TensorBinOp):
    _op_type_ = OperandDef.LDEXP
    _func_name = "ldexp"

    def _is_sparse(cls, x1, x2):
        if hasattr(x1, "issparse") and x1.issparse():
            return True
        return False

[docs]@infer_dtype(np.ldexp) def ldexp(x1, x2, out=None, where=None, **kwargs): """ Returns x1 * 2**x2, element-wise. The mantissas `x1` and twos exponents `x2` are used to construct floating point numbers ``x1 * 2**x2``. Parameters ---------- x1 : array_like Tensor of multipliers. x2 : array_like, int Tensor of twos exponents. 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 result of ``x1 * 2**x2``. See Also -------- frexp : Return (y1, y2) from ``x = y1 * 2**y2``, inverse to `ldexp`. Notes ----- Complex dtypes are not supported, they will raise a TypeError. `ldexp` is useful as the inverse of `frexp`, if used by itself it is more clear to simply use the expression ``x1 * 2**x2``. Examples -------- >>> import mars.tensor as mt >>> mt.ldexp(5, mt.arange(4)).execute() array([ 5., 10., 20., 40.], dtype=float32) >>> x = mt.arange(6) >>> mt.ldexp(*mt.frexp(x)).execute() array([ 0., 1., 2., 3., 4., 5.]) """ x2_dtype = astensor(x2).dtype casting = kwargs.get("casting", "safe") if not np.can_cast(x2_dtype, np.int64, casting=casting): raise TypeError( "ufunc 'ldexp' not supported for the input types, " "and the inputs could not be safely coerced to any supported types " f"according to the casting rule ''{casting}''" ) op = TensorLdexp(**kwargs) return op(x1, x2, out=out, where=where)