On Sat, Mar 5, 2011 at 5:10 PM, Charles R Harris
<charlesr.har...@gmail.com>wrote:
>
>
> On Fri, Mar 4, 2011 at 12:54 PM, Christoph Gohlke <cgoh...@uci.edu> wrote:
>
>>
>>
>> On 3/4/2011 1:00 AM, Christoph Gohlke wrote:
>> >
>> >
>> > On 3/3/2011 10:54 PM, Ralf Gommers wrote:
>> >> On Mon, Feb 28, 2011 at 11:31 PM, Ralf Gommers
>> >> <ralf.gomm...@googlemail.com> wrote:
>> >>> On Mon, Feb 28, 2011 at 10:36 PM, Bruce Southey<bsout...@gmail.com>
>> >>> wrote:
>> >>>> On 02/28/2011 02:00 AM, Ralf Gommers wrote:
>> >>>>> Hi,
>> >>>>>
>> >>>>> On Fri, Jan 28, 2011 at 7:15 AM, Travis
>> >>>>> Oliphant<oliph...@enthought.com> wrote:
>> >>>>>> The reason for a NumPy 1.6 suggestion, is that Mark (and others it
>> >>>>>> would
>> >>>>>> seem) have additional work and features that do not need to wait
>> >>>>>> for the
>> >>>>>> NumPy 2.0 ABI design to finalize in order to get out there.
>> >>>>>> If someone is willing to manage the release of NumPy 1.6, then it
>> >>>>>> sounds
>> >>>>>> like a great idea to me.
>> >>>>> This thread ended without a conclusion a month ago. Now I think
>> master
>> >>>>> is in a better state than a month ago for a release (py 2.4/2.5/3.x
>> >>>>> issues and segfault on OS X fixed, more testing of changes), and I
>> >>>>> have a better idea of my free time for March/April. Basically, I
>> have
>> >>>>> a good amount of time for the next couple of weeks, and not so much
>> at
>> >>>>> the end of March / first half of April due to an inter-continental
>> >>>>> move. But I think we can get out a beta by mid-March, and I can
>> manage
>> >>>>> the release.
>> >>>>>
>> >>>>> I've had a look at the bug tracker, here's a list of tickets for
>> 1.6:
>> >>>>> #1748 (blocker: regression for astype('str'))
>> >>>>> #1619 (issue with dtypes, with patch)
>> >>>>> #1749 (distutils, py 3.2)
>> >>>>> #1601 (distutils, py 3.2)
>> >>>>> #1622 (Solaris segfault, with patch)
>> >>>>> #1713 (Solaris segfault)
>> >>>>> #1631 (Solaris segfault)
>> >>
>> >> The distutils tickets are resolved.
>> >>
>> >>>>> Proposed schedule:
>> >>>>> March 15: beta 1
>> >>>>> March 28: rc 1
>> >>>>> April 17: rc 2 (if needed)
>> >>>>> April 24: final release
>> >>
>> >> Any comments on the schedule or tickets?
>> >>
>> >> Before the first beta can be released I think #1748 should be fixed.
>> >> Before the first RC the Solaris segfaults should be investigated, and
>> >> documentation for the new iterator (Python docstrings and C API docs)
>> >> and datetime should be written.
>> >>
>> >> Also, some testing on 64-bit Windows would be great, that usually
>> >> turns up new issues so the sooner the better.
>> >>
>> >> Ralf
>> >
>> > Hi Ralf,
>> >
>> > the numpy master branch on github can not be compiled with Visual
>> > Studio. A patch is attached. I'll test the builds tomorrow.
>> >
>> > Christoph
>> >
>>
>> I tested the 32 and 64 bit msvc9/MKL builds for Python 2.7 and 3.2.
>> There are few test failures (listed below) that look familiar.
>>
>> I also ran tests and/or examples of a few 3rd party packages that were
>> built against numpy 1.5.1: scipy, pygame, PyMOL, numexpr, matplotlib,
>> basemap, scikits.learn, ETS.mayavi, Bottleneck, pytables, and pandas.
>>
>> Most packages don't have any apparent problems.
>> Scipy-0.9.0-win-amd64-py3.2 and Bottleneck-0.3.0 each have one test
>> failure/error (also listed below).
>>
>> There is a problem with code generated by Cython 0.13: pytables-2.2.1
>> and pandas-0.3.0, which were built with Cython 0.13, report several
>> failures and do crash during the tests. This can probably be fixed by
>> "recythonizing" with Cython 0.14.1.
>>
>>
> The tables segfault is fixed, although other errors remain. Pandas still
> segfaults and I'm guessing that the problem is somewhere in the creation of
> object arrays/subtypes. The short code to reproduce the problem is
>
> >>> import pandas
> >>> import pandas.util.testing as common
> >>> df = common.makeTimeDataFrame()
> >>> objs = [df, df]
> >>> s = Series(objs, index=[0, 1])
>
>
>
Make that
>>> import pandas
>>> import pandas.util.testing as common
>>> df = common.makeTimeDataFrame()
>>> objs = [df, df]
>>> s = pandas.Series(objs, index=[0, 1])
Chuck
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