I am not in favor of reverting this change.

We already accounted for this in Matplotlib (
https://github.com/matplotlib/matplotlib/issues/25789 and
https://github.com/matplotlib/matplotlib/pull/25813).  It was not actually
that disruptive and mostly identified tests that were too brittle to
begin with.

My understanding is that a majority of the impact is not that the results
are inaccurate, it is that they are differently inaccurate than they used
to be.  If this is going to be reverted I think the burden should be on
those who want the reversion to demonstrate that the different results
actually matter.

Tom


On Wed, May 31, 2023 at 10:11 AM Matthew Brett <matthew.br...@gmail.com>
wrote:

> On Wed, May 31, 2023 at 3:04 PM Robert Kern <robert.k...@gmail.com> wrote:
> >
> > I would much, much rather have the special functions in the `np.*`
> namespace be more accurate than fast on all platforms. These would not have
> been on my list for general purpose speed optimization. How much time is
> actually spent inside sin/cos even in a trig-heavy numpy program? And most
> numpy programs aren't trig-heavy, but the precision cost would be paid and
> noticeable even for those programs. I would want fast-and-inaccurate
> functions to be strictly opt-in for those times that they really paid off.
> Probably by providing them in their own module or package rather than a
> runtime switch, because it's probably only a part of my program that needs
> that kind of speed and can afford that precision loss while there will be
> other parts that need the precision.
> >
>
> What Robert said :)
>
> But I still think the ideal would be the runtime option, maybe via the
> proposed context manager, for them as do need it, or want to try it
> out.
>
> Cheers,
>
> Matthew
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-- 
Thomas Caswell
tcasw...@gmail.com
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