Progress for numpy on windows 64 bits

The numpy 1.3.0 installer for windows 64 does not work very well. On some configurations, it does not even import without crashing. The crashes are mostly likely due to some bad interactions between the 64 bits mingw compilers and python (built with Visual Studio 2008). Although I know it is working, I had no interest in building numpy with MS compiler, because gfortran does not work with VS 2008. There are some incompatibilities because the fortran runtime from gfortran is incompatible with the VS 2008 C runtime (I get some scary linking errors).

So the situation is either building numpy with MS compiler, but with no hope of getting scipy afterwards, or building a numpy with crashes which are very difficult to track down. Today, I realized that I may go somewhere if somehow, I could use gfortran without using the gfortran runtime (e.g. libgfortran.a). I first tried calling a gfortran-built blas/lapack from a C program built with VS 2008, and after a couple of hours, I managed to get it working. Building numpy itself with full blas/lapack was a no-brainer then.

Now, there is the problem of scipy. Since scipy has some fortran code, which itself depends on the gfortran runtime when built with gfortran, I am trying to ‘fake’ a minimal gfortran runtime built with the C compiler. Since this mini runtime is built with the MS compiler and with the same  C runtime as used by python, it should work if the runtime is ABI compatible with the gfortran one. As gfortran is open source, this may not be intractable :)

With this technique, I could go relatively far in a short time. Among the packages which build and pass most of the test suite:
 – scipy.fftpack
 – scipy.lapack
 – some scipy.sparse

Some packages like cluster or spatial are not ANSI C compatible, so they fail to build. This should not be too hard to fix. The main problem is scipy.special: the C code is horrible, and there needs many hacks to build the C code. The Fortran code needs quite a few functions from the fortran runtime, so this needs some work. But ~ 300 unit tests of scipy pass, so this is encouraging.