[Numpy-discussion] Release of NumPy
Travis E. Oliphant
Tue Apr 15 15:35:37 CDT 2008
Sebastian Haase wrote:
> On Tue, Apr 15, 2008 at 9:41 AM, Robert Kern <email@example.com> wrote:
>> On Tue, Apr 15, 2008 at 2:31 AM, Jon Wright <firstname.lastname@example.org> wrote:
>> > Alan G Isaac wrote:
>> > >
>> > > Will matrix behavior change in 1.1, as discussed from time
>> > > to time? Perhaps it just takes a very small change in __getitem__:
>> > > <URL:http://email@example.com/msg07363.html>
>> > Quoting from:
>> > http://mail.python.org/pipermail/python-dev/2008-March/077723.html
>> > """
>> > Executive summary: Python 2.6 and 3.0 finals are planned for September
>> > 3, 2008.
>> > """
>> > In the event that there really is a valid reason to change the API (much
>> > like serial killers have their own valid reasons for their crimes...).
>> > Why not finalise that perfect API for the python3.0 build, when everyone
>> > is expecting code to change for 3K? Name them numpy3.x.x to match the
>> > python major version.
>> The Python community has been begging package owners *not* to do this.
>> That would impose yet another hurdle to the adoption of Python 3.0.
> So, to summarize one more time:
> What used to be talked about as "1.1" will now become "1.2" ---
> and "1.0.5" will be "1.1"
> Did I get this right !?
> I'm curious to know _which_ were the changes that break the API. I
> thought all additions like "axis=0" were made without breaking the
> backwards compatibility. Otherwise it would (should !!!) have been
> added as "axis=None".
> Please keep in mind that there are a number of these "awkward"
> "wrong-default" arguments. I was hoping these would be unified [e.g.
> always same default, axis=None] very soon -- that is in 1.1.
> Also on my list is N.resize vs. arr.resize, were one fills with
> zeros, the other repeats.
This is a carry over from Numeric. I should have been more proactive
here and forced the change, but I think one of the internal functions
depended on the old behavior and I punted.
So, we should allow both behaviors but I agree have them do the same
thing by default.
More information about the Numpy-discussion