[Yt-dev] Numpy warning supression

j s oishi jsoishi at gmail.com
Mon Aug 15 12:34:56 PDT 2011


Hi Stephen,

I tend to agree with Britton. I think a technically accurate but
non-informative message should be suppressed at its source sooner
rather than later. I would think exception handling in numpy should
make this possible...for Dedalus, we use numpy to dump you into
ipython if a certain kind of error occurs for deubgging purposes, so I
imagine something simpler like quieting a particular message in a
particular place should be possible...that having been said, I don't
know how to do it.

j

On Mon, Aug 15, 2011 at 12:23 PM, Britton Smith <brittonsmith at gmail.com> wrote:
> I think the issue is that these are messages coming straight out of numpy
> that are nearly impossible to supress.  My personal opinion is that they are
> annoying at best and can ruin screen output that the user might be looking
> for.  I feel that if we let these stick around, more of these will pop up.
> If at a later date someone wants to clean up the code so that the warnings
> go away, it will be much more difficult to locate them since the warning
> messages come completely without context.  I say try to get rid of it if
> possible.
>
> Britton
>
> On Mon, Aug 15, 2011 at 3:18 PM, Casey W. Stark <caseywstark at gmail.com>
> wrote:
>>
>> At the risk of introducing complexity, we could do debug levels. If
>> somebody wants these messages, they can set something, but they are
>> supressed by default.
>>
>> On Monday, August 15, 2011, Stephen Skory <s at skory.us> wrote:
>> > Hi all,
>> >
>> > this may not be a big issue because most people won't be using
>> > Geoffrey's new ellipsoidal halo information. However, it may spark
>> > discussion about this topic overall and set a useful precedent.
>> >
>> > I have just finished vectorizing some of Geoffrey's code that is
>> > attached to the halo finder code. In so doing, I am allowing NaNs to
>> > come into the calculation because it keeps thing simple. Happily, the
>> > NaNs only exist where I know the answers can't be, and
>> > numpy.nanargmin/max() happily ignores the NaNs. But when I make the
>> > NaNs through a divide by zero (and some other stuff) I get warning
>> > messages telling me I just did what I knew was going to happen.
>> >
>> > My question is, do we think that I should try to suppress these
>> > warnings? They are accurate, but the math that makes them is done
>> > intentionally, so they're not informative.
>> >
>> > Can I get a +1/0/-1? Thanks!
>> >
>> > --
>> > Stephen Skory
>> > s at skory.us
>> > http://stephenskory.com/
>> > 510.621.3687 (google voice)
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