Not exactly an answer to your question, but I can highly recommend using Boost.python, PyUblas and Ublas for your C++ vectors and matrices. It gives you a really good interface on the C++ side to numpy arrays and matrices, which can be passed in both directions over the language threshold with no copying.
If I had to guess I'd say sometimes when transposing numpy simply sets a flag internally to avoid copying the data, but in some cases (such as perhaps when multiplication needs to take place) the data has to be placed in a new object. Accessing the data via raw pointers in C++ may not be checking for the 'transpose' flag and therefore you see an unexpected result. Disclaimer: this is just a guess, someone more familiar with Numpy internals will no doubt be able to correct me. Malcolm _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
