[Numpy-discussion] Bump warning stacklevel

[Ralf Gommers]

On Wed, Jan 27, 2016 at 7:26 PM, Sebastian Berg <sebastian at sipsolutions.net>
wrote:

> Hi all,
>
> in my PR about warnings suppression, I currently also have a commit
> which bumps the warning stacklevel to two (or three), i.e. use:
>
> warnings.warn(..., stacklevel=2)
>
> (almost) everywhere. This means that for example (take only the empty
> warning):
>
> np.mean([])
>
> would not print:
>
> /usr/lib/python2.7/dist-packages/numpy/core/_methods.py:55:
> RuntimeWarning: Mean of empty slice.
>   warnings.warn("Mean of empty slice.", RuntimeWarning)
>
> but instead print the actual `np.mean([])` code line (the repetition of
> the warning command is always a bit funny).
>
> The advantage is nicer printing for the user.
>
> The disadvantage would probably mostly be that existing warning filters
> that use the `module` keyword argument, will fail.
>
> Any objections/thoughts about doing this change to try to better report
> the offending code line?


This has annoyed me for a long time, it's hard now to figure out where
warnings really come from. Especially when running something large like
scipy.test(). So +1.


> Frankly, I am not sure whether there might be
> a python standard about this, but I would expect that for a library
> such as numpy, it makes sense to change. But, if downstream uses
> warning filters with modules, we might want to reconsider for example.
>

There probably are usages of `module`, but I'd expect that it's used a lot
less than `category` or `message`. A quick search through the scipy repo
gave me only a single case where `module` was used, and that's in
deprecated weave code so soon the count is zero.  Also, even for relevant
usage, nothing will break in a bad way - some more noise or a spurious test
failure in numpy-using code isn't the end of the world I'd say.

One issue will be how to keep this consistent. `stacklevel` is used so
rarely that new PRs will always omit it for new warnings. Will we just rely
on code review, or would a private wrapper around `warn` to use inside
numpy plus a test that checks that the wrapper is used everywhere be
helpful here?

Ralf
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20160127/85ab9591/attachment.html>