# Copyright 2022-2023 XProbe Inc.
# derived from 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
#
# 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 ..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)
@property
def _fun(self):
return np.modf
@classmethod
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)
else:
out1 = None
if op.out2 is not None:
out2 = next(inputs_iter)
else:
out2 = None
if op.where is not None:
where = kw["where"] = next(inputs_iter)
else:
where = None
kw["order"] = op.order
try:
args = [input]
if out1 is not None:
args.append(out1.copy())
if out2 is not None:
args.append(out2.copy())
y1, y2 = xp.modf(*args, **kw)
except TypeError:
if where is None:
raise
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)