[SciPy-Dev] ANN: SciPy 0.10.0 beta 2

[Christoph Gohlke]

On 9/18/2011 4:02 AM, Ralf Gommers wrote:
> Hi,
>
> The first beta release of scipy 0.10.0 was, well, beta quality,
> therefore I am pleased to announce the availability of the second 0.10.0
> beta release. For this release over a 100 tickets and pull requests have
> been closed, and many new features have been added. Some of the
> highlights are:
>
>    - support for Bento as a build system for scipy
>    - generalized and shift-invert eigenvalue problems in sparse.linalg
>    - addition of discrete-time linear systems in the signal module
>
> Sources and binaries can be found at
> https://sourceforge.net/projects/scipy/files/scipy/0.10.0b2/, release
> notes are copied below. SciPy 0.10 is compatible with Python 2.4 - 3.2,
> and requires numpy 1.5.1 or higher.
>
> Please try this release and report problems on the mailing list.
>
> Cheers,
> Ralf
>
>

Hello Ralf,

looks good. Just some minor issues:

1) I was unable to build from scipy-0.10.0b2.zip: "error: 
src\fblaswrap.f: No such file or directory". The git branch is OK.

2) Can the file japanese_utf8.txt in scipy\io\matlab\tests\data somehow 
be marked as binary on github? It is automatically converted to Windows 
line endings such that test_mio.test_load fails.

3) Building with Intel C compiler fails on Windows due to a bug in 
Cython 0.15, which has recently been fixed 
<https://github.com/cython/cython/commit/0443ad3d55f0a4762d4009bc606cb98ee4f4a1d6>. 
The following files are affected:

interpolate/interpnd.c
io/matlab/mio5_utils.c
io/matlab/mio_utils.c
io/matlab/streams.c
spatial/ckdtree.c
spatial/qhull.c


4) FAIL: test_datatypes.test_uint64_max
----------------------------------------------------------------------
Traceback (most recent call last):
   File "X:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
     self.test(*self.arg)
   File 
"X:\Python27\lib\site-packages\scipy\ndimage\tests\test_datatypes.py", 
line 57, in test_uint64_max
     assert_true(x[1] > (2**63))
AssertionError: False is not true


This is due to the 32 bit visual C compiler using signed int64 when 
converting between uint64 to double.  Anyway, it seems unreliable to me 
to cast back and forth between uint64 and double types for large numbers 
because of potential overflow and precision loss.


5) Two known failures in test_arpack.test_complex_nonsymmetric_modes and 
test_arpack.test_symmetric_modes.
Ticket 1515 <http://projects.scipy.org/scipy/ticket/1515>.

Christoph