[Numpy-discussion] Numpy v1.3.0b1 on Linux w/ Intel compilers - unknown file type

Christian Marquardt christian@marquardt...
Thu Mar 26 21:49:54 CDT 2009


Oh sorry - you are right (too late in the night here in Europe). 

The output is similar in all four cases - it looks like 

AssertionError: 
Arrays are not almost equal 

(mismatch 100.0%) 
x: array([ 4.60555124+0.j, -2.60555124+0.j], dtype=complex64) 
y: array([-2.60555124 +1.11022302e-16j, 4.60555124 -1.11022302e-16j], dtype=complex64) 

Are x and y the expected and actual results? That would just show that there 
are small rounding errors in the imaginary part, and that MKL returns the results 
in another order, no? 


----- "Charles R Harris" <charlesr.harris@gmail.com> wrote: 
> 
> 
> 
> 2009/3/26 Christian Marquardt < christian@marquardt.sc > 
> 



> 
> Hmm. 
> 
> I downloaded the beta tar file and started from the untarred contents plus a patch for the Intel compilers 
> (some changes of the command line arguments for the compiler and a added setup.cfg file specifying the 
> paths to the Intel MKL libraries) which applied cleanly. I then ran 
> 
> python setup.py config --compiler=intel config_fc --fcompiler=intel build_clib --compiler=intel build_ext --compiler=intel install 
> 
> which failed. 
> 
> After playing around a bit, I found that it seems that the build_clib --compiler=intel subcommand which 
> causes the trouble; after disabling it, that is with 
> 
> python setup.py config --compiler=intel config_fc --fcompiler=intel build_ext --compiler=intel install 
> 
> things compile fine - and all but four of the unit tests fail (test_linalg.TestEigh and test_linalg.TestEigvalsh 
> in both test_csingle and test_cdouble - should I be worried?) 
> 

> Four unit tests fail, or all fail except four? I assume you meant the former. I'm not sure what the failures mean, can you check if they are really bad or just some numbers a little bit off. I'm guessing these routines are calling into MKL. 
> 
> 



> 
> 
> How are the .src files converted? 
> 

> The *.xxx.src files are templates that are processed by numpy/distutils/conv_template.py to produce *.xxx files. When you have to repeat basically the same code with umpteen different types a bit of automation helps. The actual conversion is controlled by the setup/scons files, I don't remember exactly where. 
> 
> Chuck 
> 
> 
> 
> _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion 

-- 
Dr. Christian Marquardt Email: christian@marquardt.sc 
Wilhelm-Leuschner-Str. 27 Tel.: +49 (0) 6151 95 13 776 
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