# 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 ..datasource import tensor as astensor
from .core import TensorReduction, TensorReductionMixin
class TensorNanSum(TensorReduction, TensorReductionMixin):
_op_type_ = OperandDef.NANSUM
_func_name = "nansum"
def __init__(self, axis=None, keepdims=None, combine_size=None, stage=None, **kw):
stage = self._rewrite_stage(stage)
super().__init__(
_axis=axis,
_keepdims=keepdims,
_combine_size=combine_size,
stage=stage,
**kw
)
[docs]def nansum(a, axis=None, dtype=None, out=None, keepdims=None, combine_size=None):
"""
Return the sum of array elements over a given axis treating Not a
Numbers (NaNs) as zero.
Zero is returned for slices that are all-NaN or
empty.
Parameters
----------
a : array_like
Tensor containing numbers whose sum is desired. If `a` is not an
tensor, a conversion is attempted.
axis : int, optional
Axis along which the sum is computed. The default is to compute the
sum of the flattened array.
dtype : data-type, optional
The type of the returned tensor and of the accumulator in which the
elements are summed. By default, the dtype of `a` is used. An
exception is when `a` has an integer type with less precision than
the platform (u)intp. In that case, the default will be either
(u)int32 or (u)int64 depending on whether the platform is 32 or 64
bits. For inexact inputs, dtype must be inexact.
out : Tensor, optional
Alternate output tensor in which to place the result. The default
is ``None``. If provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
`doc.ufuncs` for details. The casting of NaN to integer can yield
unexpected results.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
If the value is anything but the default, then
`keepdims` will be passed through to the `mean` or `sum` methods
of sub-classes of `Tensor`. If the sub-classes methods
does not implement `keepdims` any exceptions will be raised.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
nansum : Tensor.
A new tensor holding the result is returned unless `out` is
specified, in which it is returned. The result has the same
size as `a`, and the same shape as `a` if `axis` is not None
or `a` is a 1-d array.
See Also
--------
mt.sum : Sum across tensor propagating NaNs.
isnan : Show which elements are NaN.
isfinite: Show which elements are not NaN or +/-inf.
Notes
-----
If both positive and negative infinity are present, the sum will be Not
A Number (NaN).
Examples
--------
>>> import mars.tensor as mt
>>> mt.nansum(1).execute()
1
>>> mt.nansum([1]).execute()
1
>>> mt.nansum([1, mt.nan]).execute()
1.0
>>> a = mt.array([[1, 1], [1, mt.nan]])
>>> mt.nansum(a).execute()
3.0
>>> mt.nansum(a, axis=0).execute()
array([ 2., 1.])
>>> mt.nansum([1, mt.nan, mt.inf]).execute()
inf
>>> mt.nansum([1, mt.nan, mt.NINF]).execute()
-inf
>>> mt.nansum([1, mt.nan, mt.inf, -mt.inf]).execute() # both +/- infinity present
nan
"""
a = astensor(a)
if dtype is None:
dtype = np.nansum(np.empty((1,), dtype=a.dtype)).dtype
op = TensorNanSum(
axis=axis, dtype=dtype, keepdims=keepdims, combine_size=combine_size
)
return op(a, out=out)