[Numpy-discussion] 1.5.x branched

[Christoph Gohlke]

On 7/18/2010 7:52 AM, Pauli Virtanen wrote:
> Sun, 18 Jul 2010 21:13:43 +0800, Ralf Gommers wrote:
> [clip]
>> Builds fine on OS X 10.6 with both 2.7 and 3.1, and all tests pass. With
>> one exception: in-place build for 3.1 is broken. Does anyone know is
>> this is a distutils or numpy issue? The problem is that on import
>> numpy.__config__ can not be found.
>
> The inplace build goes to
>
> 	build/py3k
>
> I don't think we can easily support inplace build in the main directory.
>
> [clip]
>> Don't think that's going to happen, but it would be good to discuss the
>> release schedule. What changes need to go in before a first beta, and
>> how much time do you all need for that?
>
> I don't have anything urgent.
>
> It might be good to glance through the tickets, however, to see if
> there's something serious pending.
>
> Also, I'm not 100% sure what's the Python 3 status on Windows, so that
> should be checked too.
>

I tested MSVC builds of numpy 1.5.0.dev8500 on 32 and 64 bit Python 2.5, 
2.6, 2.7, and 3.1 for Windows.

I general all looks good, including the Python 3 builds.

Numpy 1.5 builds OK with msvc after applying the patch at 
http://projects.scipy.org/numpy/attachment/ticket/1502/msvc_isnan_isinf.patch

Only a few tests fail. Except for one error 
(http://projects.scipy.org/numpy/ticket/1499), all failed tests can 
probably be marked as known. The 32 bit Python 2.5 build shows some 
additional failures due to inf/nan issues but I don't really care enough 
to report them here.

A lot of warnings are printed during the test. Can they be disabled in 
the final release?
Warning: invalid value encountered in sign
Warning: invalid value encountered in fmax
Warning: invalid value encountered in fmin
Warning: invalid value encountered in logaddexp
Warning: invalid value encountered in logaddexp2
Warning: invalid value encountered in maximum
Warning: invalid value encountered in minimum
Warning: invalid value encountered in absolute
Warning: invalid value encountered in equal
Warning: invalid value encountered in less



======================================================================
ERROR: test_filename (test_memmap.TestMemmap)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"X:\Python26-x64\lib\site-packages\numpy\core\tests\test_memmap.py", 
line 60, in test_filename
     os.unlink(tmpname)
WindowsError: [Error 32] The process cannot access the file because it 
is being used by another process: 
'c:\\users\\gohlke\\appdata\\local\\temp\\mmapgl7bkg'


This error could be due to a Python bug: 
http://bugs.python.org/issue7443. I tried the proposed patches for 
issue7443 without success.


======================================================================
ERROR: Ticket #99
----------------------------------------------------------------------
Traceback (most recent call last):
   File "X:\Python26-x64\lib\site-packages\numpy\testing\decorators.py", 
line 215, in knownfailer
     return f(*args, **kwargs)
   File 
"X:\Python26-x64\lib\site-packages\numpy\core\tests\test_regression.py", 
line 145, in test_intp
     np.intp('0x' + 'f'*i_width,16)
TypeError: function takes at most 1 argument (2 given)


Fails on 64 bit Python 2.x only. This test is disabled for Python 3.0+. 
It seems np.intp does not inherit from Python long on win-amd64 
platforms. This can probably be marked as knownfailure for win-amd64. 
See http://projects.scipy.org/numpy/ticket/99.


======================================================================
ERROR: test_ldexp (test_umath.TestLdexp)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"X:\Python26-x64\lib\site-packages\numpy\core\tests\test_umath.py", line 
363, in test_ldexp
     assert_almost_equal(ncu.ldexp(np.array(2., np.longdouble), 
np.array(3, np.int16)), 16.)
TypeError: function not supported for these types, and can't coerce 
safely to supported types


This also fails for Python 2.5.4, Solaris 8, sparc 32 bits. See 
http://projects.scipy.org/numpy/ticket/1499


======================================================================
FAIL: test_special_values (test_umath_complex.TestClog)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"X:\Python26-x64\lib\site-packages\numpy\core\tests\test_umath_complex.py", 
line 224, in test_special_values
     assert_almost_equal(np.log(x), y)
   File "X:\Python26-x64\lib\site-packages\numpy\testing\utils.py", line 
443, in assert_almost_equal
     raise AssertionError(msg)
AssertionError:
Arrays are not almost equal
  ACTUAL: array([ NaN+2.35619449j])
  DESIRED: (inf+2.3561944901923448j)
>>  raise AssertionError('\nArrays are not almost equal\n ACTUAL: array([ NaN+2.35619449j])\n DESIRED: (inf+2.3561944901923448j)')


This can probably be marked as a known failure again (as in 1.4.1).


======================================================================
FAIL: test_doctests (test_polynomial.TestDocs)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"X:\Python25-x64\Lib\site-packages\numpy\lib\tests\test_polynomial.py", 
line 90, in test_doctests
     return rundocs()
   File "X:\Python25-x64\Lib\site-packages\numpy\testing\utils.py", line 
962, in rundocs
     raise AssertionError("Some doctests failed:\n%s" % "\n".join(msg))
AssertionError: Some doctests failed:
**********************************************************************
File 
"X:\python25-x64\lib\site-packages\numpy\lib\tests\test_polynomial.py", 
line 20, in test_polynomial
Failed example:
     print(poly1d([100e-90, 1.234567e-9j+3, -1234.999e8]))
Expected:
            2
     1e-88 x + (3 + 1.235e-09j) x - 1.235e+11
Got:
             2
     1e-088 x + (3 + 1.235e-009j) x - 1.235e+011

>>  raise AssertionError("Some doctests failed:\n%s" % "\n".join(['*************


This Python 2.5 specific doctest failure is due to differences in print 
output. Can this be marked as a known failure for Python 2.5?


--
Christoph