Why does `np.sum([np.Inf, +np.Inf])` warn about "invalid value encountered in reduce"
Question:
python c "import numpy as np; print(np.sum([np.Inf, +np.Inf]))"
gives
numpycorefromnumeric.py:86: RuntimeWarning: invalid value encountered in reduce
return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
nan
I wonder why that is:

There is no warning in
python c "import numpy as np; print(np.sum([np.Inf, np.Inf]))"
nor in
python c "import numpy as np; print(np.sum([+np.Inf, +np.Inf]))"
so it can’t be the
Inf
s. 
There is no warning in
python c "import numpy as np; print(np.sum([np.nan, np.nan]))"
so it can’t be the
NaN
result.
What is it, then, and how can I avoid it? I actually like getting NaN
as a result, I just want to avoid the warning.
Answers:
The warning is fine, because Inf  Inf
is mathematically undefined. What result would you expect?
If you want to avoid the warning, use a filter as follows:
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
res = np.sum([np.Inf, np.Inf])
It turns out the answer by @CarlosHorn is pretty close, although hidden deep inside the IEEE standard 754 (I checked the 2008 version).
Section 7.2 (Default exception handling > Invalid operation) writes
The invalid operation exception is signaled if and only if there is no usefully definable result.
I wouldn’t know what a "usefully definable result" might be, considering that some people may not find Inf
useful; I, by contrast, find even NaN
pretty useful. Anyay, the section gives a comprehensive list of examples, which includes (in d) the "magnitude subtraction of infinities", explaining why and form of Inf  Inf
should be considered invalid. It does also include (in a) "any […] operation on a signaling NaN", but does not include operations on a quiet NaN. This important distinction explains why NaN + NaN
usually does not signal, as np.nan
is quiet.
For completeness, section 6.1 explains why Inf + Inf
should not be considered invalid.
Two things left to says:
 It is unclear (yet irrelevant) to me why
np.inf  np.inf
does not raise an exception. with np.errstate(invalid="ignore"): ...
is probably the cleanest way to suppress the warning.
More resources: