[Numpy-discussion] Release of NumPy

Sebastian Haase haase@msg.ucsf....
Tue Apr 15 02:58:07 CDT 2008


On Tue, Apr 15, 2008 at 9:41 AM, Robert Kern <robert.kern@gmail.com> wrote:
> On Tue, Apr 15, 2008 at 2:31 AM, Jon Wright <wright@esrf.fr> 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://www.mail-archive.com/numpy-discussion@scipy.org/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.
{{{
>>> N.resize([1,2,3], 6)
[1 2 3 1 2 3]
>>> a=N.array([1,2,3]);a.resize(6);a
[1 2 3 0 0 0]
}}}
very confusing !! Please speak out !

Regards and thanks you all for the good/hard work !
-Sebastian Haase


More information about the Numpy-discussion mailing list