This is good to hear. I presume that the AMD64 is covered.
On 22-Jan-15 4:29 PM, Carl Kleffner wrote:
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.
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