[SciPy-Dev] Scipy 1.0 roadmap
Sat Sep 21 18:12:59 CDT 2013
On Sat, Sep 21, 2013 at 6:35 PM, Nathaniel Smith <email@example.com> wrote:
> On Sat, Sep 21, 2013 at 7:54 PM, Ralf Gommers <firstname.lastname@example.org> wrote:
>> - topics like "do we need a roadmap?" or "what does 1.0-ready really mean?"
>> are discussed on this thread.
> I would be curious what the answers are to these questions :-).
> This looks like a big list with many good improvements on it, but I'm
> not sure what makes them "1.0 changes" instead of just "good changes
> we should do". Does 1.0 mean we can break a lot of stuff at once and
> get away with it? Does it mean that after that we're not allowed to
> change things like this ever again so we have to get it right (and
> maybe keep slipping the schedule until we're certain)?
I haven't been to EuroScipy so I don't know the original discussion.
The main difference for me would be that I expect more stability
afterwards. Currently we still need to clean up code that requires
deprecation and API changes pretty regularly (or they are cumbersome
to cleanup while maintaining backwards compatibility.)
Several parts of scipy have "organically grown", sometimes with
several competing and incompatible implementations, some parts of the
code were never reviewed and tested.
IMO, these are the main problems with the current code that need to be
cleaned up before we can call it a scipy 1.0.
In the mean time, we still get new code, missing algorithms and
improvements in many sub-packages.
> Does it mean
> no more releases until all the below things happen? Or on the other
> extreme, does it mean that someone will keep an eye on this list, and
> at some point maybe in a few years when we notice that all of these
> things have happened, then the next release gets called "1.0" instead
> of "0.18" or whatever?
I think it will be the latter, keep working as usual, with an eye or
priority on the things on the list, release at a pretty regular
How fast we can check off the items on the roadmap to 1.0 list will
depend on volunteer work.
Positive case: wrong skew and kurtosis in stats.distributions has
already been fixed, thanks to Evgeni's recent work.
stats.distributions are in pretty good shape now and some of the
remaining issues are bonus points that don't need to hold up a 1.0.
Some other parts of scipy.stats have test coverage in the 60%-70%
which is also a rough estimate how many functions still need to be
reviewed, tested and cleaned up (30%?) (cleanup or delete legacy code?
Once we have these functions in 1.0 condition, I don't expect that
they will change much anymore.
Once a t-test has been properly written, we don't need to change it
anymore, (we can still add a few more bells).
My examples are stats, but the same applies (at least) to signal and
> I think when you start talking about "1.0" people have very strong
> conflicting assumptions about what this "obviously" means, so...
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