Source code for mars.tensor.arithmetic.fmod

#!/usr/bin/env python
# -*- coding: utf-8 -*-
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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)