I took time to create mingw-w64 based wheels of numpy-1.9.1 and
scipy-0.15.1 source distributions and put them on
https://bitbucket.org/carlkl/mingw-w64-for-python/downloads as well as on
binstar.org. The test matrix is python-2.7 and 3.4 for both 32bit and
64bit.

Feedback is welcome.

The wheels can be pip installed with:

pip install -i https://pypi.binstar.org/carlkl/simple numpy
pip install -i https://pypi.binstar.org/carlkl/simple scipy

Some technical details: the binaries are build upon OpenBLAS as accelerated
BLAS/Lapack. OpenBLAS itself is build with dynamic kernels (similar to MKL)
and automatic runtime selection depending on the CPU. The minimal requested
feature supplied by the CPU is SSE2. SSE1 and non-SSE CPUs are not
supported with this builds. This is the default for 64bit binaries anyway.

OpenBLAS is deployed as part of the numpy wheel. That said, the scipy
wheels mentioned above are dependant on the installation of the OpenBLAS
based numpy and won't work i.e. with an installed  numpy-MKL.

For the numpy 32bit builds there are 3 failures for special FP value tests,
due to a bug in mingw-w64 that is still present. All scipy versions show up
7 failures with some numerical noise, that could be ignored (or corrected
with relaxed asserts in the test code).

PR's for numpy and scipy are in preparation. The mingw-w64 compiler used
for building can be found at
https://bitbucket.org/carlkl/mingw-w64-for-python/downloads.