Trying to debug code written by an undergrad working for a colleague of mine who ported code over from MATLAB, I am seeing an ugly melange of matrix objects and ndarrays that are interacting poorly with each other and various functions in SciPy/other libraries. In particular there was a custom minimizer function that contained a line "a * b", that was receiving an Nx1 "matrix" and a N-length array and computing an outer product. Hence the unexpected 6 GB of memory usage and weird results...
We've had this discussion before and it seems that the matrix class isn't going anywhere (I *really* wish it would at least be banished from the top-level namespace), but it has its adherents for pedagogical reasons. Could we at least consider putting a gigantic warning on all the functions for creating matrix objects (matrix, mat, asmatrix, etc.) that they may not behave quite so predictably in some situations and should be avoided when writing nontrivial code?
There are already such warnings scattered about on SciPy.org but the situation is so bad, in my opinion (bad from a programming perspective and bad from a new user perspective, asking "why doesn't this work? why doesn't that work? why is this language/library/etc. so stupid, inconsistent, etc.?") that the situation warrants steering people still further away from the matrix object.
I apologize for ranting, but it pains me when people give up on Python/NumPy because they can't figure out inconsistencies that aren't really there for a good reason. IMHO, of course.