[SciPy-Dev] [SciPy-User] ANN: SciPy 0.10.1 release candidate 1

Paul Anton Letnes paul.anton.letnes@gmail....
Tue Feb 21 01:29:37 CST 2012


> I'm not sure what to make of these. I'm tempted to say that for now only the recommended gfortran is supported. There's too much going wrong on OS X Lion to be able to fix it all for 0.10.1.
> 
> For 0.11.0 we should attempt to get this fixed, including the llvm-gcc situation.
> 
> Can you check where that gfortran 4.6.2 actually comes from?
> 
> Ralf

After rebuilding with the recommended gfortran-4.2 this is the output from the tests. It does look better now. Let me know if you want me to try something else (like verbose=2). Oh, and by the way - this is 0.10.rc2.

Cheers
Paul

Tests:

(scipy-test)i-courant ~/Downloads % python -c 'import scipy;scipy.test(verbose=1)'
Running unit tests for scipy
NumPy version 1.6.1
NumPy is installed in /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/numpy
SciPy version 0.10.1rc2
SciPy is installed in /Users/paulanto/Downloads/scipy-0.10.1rc2/scipy-test/lib/python2.7/site-packages/scipy
Python version 2.7.2 (default, Oct  9 2011, 18:03:13) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]
nose version 1.1.2
............................................................................................................................................................................................................................K............................................................................................................/Users/paulanto/Downloads/scipy-0.10.1rc2/scipy-test/lib/python2.7/site-packages/scipy/interpolate/fitpack2.py:674: UserWarning: 
The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank deficient
system (deficiency=7). If deficiency is large, the results may be
inaccurate. Deficiency may strongly depend on the value of eps.
  warnings.warn(message)
....../Users/paulanto/Downloads/scipy-0.10.1rc2/scipy-test/lib/python2.7/site-packages/scipy/interpolate/fitpack2.py:605: UserWarning: 
The required storage space exceeds the available storage space: nxest
or nyest too small, or s too small.
The weighted least-squares spline corresponds to the current set of
knots.
  warnings.warn(message)
........................K..K....../usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/numpy/core/numeric.py:1920: RuntimeWarning: invalid value encountered in absolute
  return all(less_equal(absolute(x-y), atol + rtol * absolute(y)))
............................................................................................................................................................................................................................................................................................................................................................................................................................................./Users/paulanto/Downloads/scipy-0.10.1rc2/scipy-test/lib/python2.7/site-packages/scipy/io/wavfile.py:31: WavFileWarning: Unfamiliar format bytes
  warnings.warn("Unfamiliar format bytes", WavFileWarning)
/Users/paulanto/Downloads/scipy-0.10.1rc2/scipy-test/lib/python2.7/site-packages/scipy/io/wavfile.py:121: WavFileWarning: chunk not understood
  warnings.warn("chunk not understood", WavFileWarning)
...............................................................................................................................................................................................................................SSSSSS......SSSSSS......SSSS...............................................................................S............................................................................................................................................................................................................................................................K......................................................................................................................................................................................................SSSSS............S..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................SSSSSSSSSSS.........../Users/paulanto/Downloads/scipy-0.10.1rc2/scipy-test/lib/python2.7/site-packages/scipy/sparse/linalg/eigen/arpack/arpack.py:63: UserWarning: Single-precision types in `eigs` and `eighs` are not supported currently. Double precision routines are used instead.
  warnings.warn("Single-precision types in `eigs` and `eighs` "
............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K...............................................................K...........................................................................................................................................................KK.............................................................................................................................................................................................................................................................................................................................................................................................................................................K.K.............................................................................................................................................................................................................................................................................................................................................................................................K........K..............SSSSSSS..........................................................................................................................................................S..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
----------------------------------------------------------------------
Ran 5101 tests in 53.247s

OK (KNOWNFAIL=12, SKIP=42)



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