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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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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="binary_or")
class TensorFMod(TensorBinOp):
_op_type_ = OperandDef.FMOD
_func_name = "fmod"
[docs]@infer_dtype(np.fmod)
def fmod(x1, x2, out=None, where=None, **kwargs):
"""
Return the element-wise remainder of division.
This is the NumPy implementation of the C library function fmod, the
remainder has the same sign as the dividend `x1`. It is equivalent to
the Matlab(TM) ``rem`` function and should not be confused with the
Python modulus operator ``x1 % x2``.
Parameters
----------
x1 : array_like
Dividend.
x2 : array_like
Divisor.
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
For other keyword-only arguments, see the
:ref:`ufunc docs <ufuncs.kwargs>`.
Returns
-------
y : Tensor_like
The remainder of the division of `x1` by `x2`.
See Also
--------
remainder : Equivalent to the Python ``%`` operator.
divide
Notes
-----
The result of the modulo operation for negative dividend and divisors
is bound by conventions. For `fmod`, the sign of result is the sign of
the dividend, while for `remainder` the sign of the result is the sign
of the divisor. The `fmod` function is equivalent to the Matlab(TM)
``rem`` function.
Examples
--------
>>> import mars.tensor as mt
>>> mt.fmod([-3, -2, -1, 1, 2, 3], 2).execute()
array([-1, 0, -1, 1, 0, 1])
>>> mt.remainder([-3, -2, -1, 1, 2, 3], 2).execute()
array([1, 0, 1, 1, 0, 1])
>>> mt.fmod([5, 3], [2, 2.]).execute()
array([ 1., 1.])
>>> a = mt.arange(-3, 3).reshape(3, 2)
>>> a.execute()
array([[-3, -2],
[-1, 0],
[ 1, 2]])
>>> mt.fmod(a, [2,2]).execute()
array([[-1, 0],
[-1, 0],
[ 1, 0]])
"""
op = TensorFMod(**kwargs)
return op(x1, x2, out=out, where=where)