# Source code for mars.tensor.reduction.nanmax

```
#!/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
#
# 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.
from ... import opcodes as OperandDef
from ..datasource import tensor as astensor
from .core import TensorReduction, TensorReductionMixin
class TensorNanMax(TensorReduction, TensorReductionMixin):
_op_type_ = OperandDef.NANMAX
_func_name = "nanmax"
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 nanmax(a, axis=None, out=None, keepdims=None, combine_size=None):
"""
Return the maximum of an array or maximum along an axis, ignoring any
NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is
raised and NaN is returned for that slice.
Parameters
----------
a : array_like
Tensor containing numbers whose maximum is desired. If `a` is not a
tensor, a conversion is attempted.
axis : int, optional
Axis along which the maximum is computed. The default is to compute
the maximum of the flattened tensor.
out : ndarray, optional
Alternate output array 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.
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 `max` method
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
-------
nanmax : Tensor
A tensor with the same shape as `a`, with the specified axis removed.
If `a` is a 0-d tensor, or if axis is None, a Tensor scalar is
returned. The same dtype as `a` is returned.
See Also
--------
nanmin :
The minimum value of a tensor along a given axis, ignoring any NaNs.
amax :
The maximum value of a tensor along a given axis, propagating any NaNs.
fmax :
Element-wise maximum of two tensors, ignoring any NaNs.
maximum :
Element-wise maximum of two tensors, propagating any NaNs.
isnan :
Shows which elements are Not a Number (NaN).
isfinite:
Shows which elements are neither NaN nor infinity.
amin, fmin, minimum
Notes
-----
Mars uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.
If the input has a integer type the function is equivalent to np.max.
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.array([[1, 2], [3, mt.nan]])
>>> mt.nanmax(a).execute()
3.0
>>> mt.nanmax(a, axis=0).execute()
array([ 3., 2.])
>>> mt.nanmax(a, axis=1).execute()
array([ 2., 3.])
When positive infinity and negative infinity are present:
>>> mt.nanmax([1, 2, mt.nan, mt.NINF]).execute()
2.0
>>> mt.nanmax([1, 2, mt.nan, mt.inf]).execute()
inf
"""
a = astensor(a)
op = TensorNanMax(
axis=axis, dtype=a.dtype, keepdims=keepdims, combine_size=combine_size
)
return op(a, out=out)
```