Processing TerraSAR-X in Python and R

Finally some 12GB of TerraSAR-X Quad-pol data finished downloading. I have essentially 3 PolInSAR capable sets (if the notorious X-band coherence holds up). I can process them in RAT but that requires converting them to floating point and more bloat, the TSX data is in a CInt32 form. Solution was to use GDAL's TerraSAR-X Cosar support and read subsets directly into Python. I took this opportunity to do some nice unit testing in Python. The result was the simple monstrosity here. It extends tsx_dual class I had implemented before.

Then I moved onto some phase difference statistics and encountered for the n-th time the nicely non-Gaussian von Mises distribution. Trying to use R Circular Statistics from Python became the bane of my existence tonight. RPy2 current code is unsupported on windows so I am lacking package import. At least I managed to hack it into compiling with the following tricks:

1. Copied Rinterface.h from R source distro
2. Hacked Rinterface.h to remove uintptr_t typedef
3. Hacked na_values.c to remove dynamic allocations (compile time non-constants)
4. Copied R dll's from "bin" to "lib" in R install

This makes rpy2 build and install, but I am left with - Assertion failed: PyType_IsSubtype(type, &PyLong_Type), file rpy\rinterface\/na_values.c, line 166  - at runtime obviously due to my hacks. Well I am missing this code block in my hand hacked things:

/* on some platforms these are not compile-time constants, so we must fill them at runtime */
+  NAInteger_Type.tp_base = &PyInt_Type;
+  NALogical_Type.tp_base = &PyInt_Type;
+  NAReal_Type.tp_base = &PyFloat_Type;
+  NACharacter_Type.tp_base = &PyString_Type;

Now to figure out where to put it and von Mises are in Python.

PS: So it is "Talk like a Pirate Day" so "R - ARRR in".