[AnimalMuppet]

Off topic, but this seems like as good a place as any to ask: It's my impression that numpy is really good. Is it as good as Fortran? That is, if I have a large, sparse, complex matrix, Fortran will have an efficient solver for it that will also be numerically stable, and will have four decades of use to find any weaknesses. Is numpy equivalent (except for the four decades part)? Is it close? Or does it just cover the basic cases well, and for the specializations you're on your own?

[zb]

NumPy is designed to work with SciPy, which is a wrapper for literally the same 4 decade old Fortran libraries (LAPACK &c.) that you're referring to here.

[gnufx]

Note that LAPACK isn't four decades old (and probably should be superseded anyway). You perhaps won't want to use large-scale numerical libraries that are that old unless they've had a lot of development and been well parallelized.

One issue with mixed language programming that we learned decades ago is the issue of debugging that there typically is across the interface. Also general tool support. I recently asked the local Python expert about HPC-style profiling of Python calling C(++) libraries, for instance. (I couldn't make TAU work.)