[Numpy-discussion] Numpy 2.0 schedule

Bruce Southey bsouthey@gmail....
Mon Feb 28 08:36:05 CST 2011


On 02/28/2011 02:00 AM, Ralf Gommers wrote:
> Hi,
>
> On Fri, Jan 28, 2011 at 7:15 AM, Travis Oliphant<oliphant@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)
>
> I can look at the distutils tickets.
>
> The other thing that needs to be done is some (more) documentation of
> new features. Einsum and the new iterator seem to be well documented,
> but not described in the release notes. Datetime has no docs as far as
> I can see except for two similar NEPs.
>
> Proposed schedule:
> March 15: beta 1
> March 28: rc 1
> April 17: rc 2 (if needed)
> April 24: final release
>
> Let me know what you think. Bonus points for volunteering to fix some
> of those tickets:)
>
> Cheers,
> Ralf
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Is this 1.6 or 2.0?
The title is 2.0 but you talk about 1.6 so some tickets listed as 2.0 
may apply to 1.6.

It would be great to do some 'housekeeping' and try to address some of 
the old tickets dealt with before numpy 2.0. For example, I think ticket 
225 (bincount does not accept input of type > N.uint16) has been 
addressed but it needs to be checked from windows and 32-bit systems.

Bruce

Created 2006:
#38    strides accepted as an argument to records.array
#57    ufunc methods need improved BUFFER loop
#213    SharedLibrary builder for numpy.distutils
#225    bincount does not accept input of type > N.uint16
#236    reduceat cornercase
#237    reduceat should handle outlier indices gracefully
#244    Build fails with Intel Visual Fortran compiler
#260    Add mechanism for registering objects to be deallocated and 
memory-to-be freed at Python exit
#274    Speed up N-D Boolean indexing
#301    power with negative argument returns 0
#333    Creating an array from a n-dim dtype type fails
#338    Valgrind warning when calling scipy.interpolate.interp1d
#349    Improve unit tests in linalg
#354    Possible inconsistency in 0-dim and scalar empty array types
#398    Compatibility loader for old Numeric pickles
#400    C API access to fft for C scipy extension ?
#402    newaxis incompatible with array indexing


Numpy 1.0
#450    Make a.min() not copy data
#417    Numpy 1.0.1 compilation fails on IRIX 6.5
#527    fortran linking flag option...
#1176    deepcopy turns ndarry into string_
#1143    Improve performance of PyUFunc_Reduce
#931    Records containing zero-length items pickle just fine, but 
cannot be unpickled
#803    Assignment problem on matrix advanced selection

Numpy 1.1
#1266    Extremely long runtimes in numpy.fft.fft
#963    Object array comparisons eat exceptions
#929    empty_like and zeros_like behave differently from ones_like
#934    Documentation error in site.cfg.example

Numpy 1.2
#1374    Ticket 628 not fixed for Solaris (polyfit uses 100% CPU and 
does not stop)
#1209    Docstring for numpy.numarray.random_array.multinomial is out of 
date.
#1192    integer dot product
#1172    abs does not work with -maxint
#1163    Incorrect conversion to Int64 by loadtxt (traced to _getconv in 
numpy.lib.io)
#1161    Errors and/or wrong result with reverse slicing in numpy.delete
#1094    masked array autotest fails with bus error
#1085    Surprising results from in-place operations involving views
#1071    loadtxt fails if the last column contains empty value





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