I made numpy master (numpy-1.11.0.dev0 , https://github.com/numpy/numpy/commit/0243bce23383ff5e894b99e40df2f8fd806ad79f) windows binary wheels available for testing.
Install it with pip: > pip install -i https://pypi.anaconda.org/carlkl/simple numpy These builds are compiled with OPENBLAS trunk for BLAS/LAPACK support and the mingwpy compiler toolchain. OpenBLAS is deployed within the numpy wheels. To be performant on all usual CPU architectures OpenBLAS is configured with it's 'dynamic architecture' and automatic CPU detection. This version of numpy fakes long double as double just like the MSVC builds. Some test statistics: win32 (32 bit) numpy-1.11.0.dev0, python-2.6: errors=8, failures=1 numpy-1.11.0.dev0, python-2.7: errors=8, failures=1 numpy-1.11.0.dev0, python-3.3: errors=9 numpy-1.11.0.dev0, python-3.4: errors=9 amd64 (64bit) numpy-1.11.0.dev0, python-2.6: errors=9, failures=6 numpy-1.11.0.dev0, python-2.7: errors=9, failures=6 numpy-1.11.0.dev0, python-3.3: errors=10, failures=6 numpy-1.11.0.dev0, python-3.4: errors=10, failures=6 Carl
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