Hi, In order to allow in-kernel queries in PyTables (www.pytables.org) work with unsigned 64-bit integers, we would like to see uint64 support in Numexpr (http://code.google.com/p/numexpr/).
To do this, we have to decide first how uint64 interacts with other types. For example, which should be the outcome of: numpy.array([1], 'int64') / numpy.array([2], 'uint64') Basically, there are a couple of possibilities: 1) To follow the behaviour of NumPy and upcast both operands to float64 and do the operation. That is: In [21]: numpy.array([1], 'int64') / numpy.array([2], 'uint64') Out[21]: array([ 0.5]) 2) Implement support for uint64 as a non-upcastable type, so that one cannot merge uint64 operands with other types. That is: In [21]: numpy.array([1], 'int64') / numpy.array([2], 'uint64') Out[21]: TypeError: unsupported operand type(s) for /: 'int64' and 'uint64' Solution 1) is appealing because is how NumPy works, but I don't personally like the upcasting to float64. First of all, because you transparently convert numbers potentially loosing the least significant digits. Second, because an operation between integers gives a float as a result, and this is different for typical programming languages. Solution 2) addresses shortcomings of solution 1), but introduces the problem that can only operate in conjunction with other uint64 operands, making it practically an 'isolated' type (much like a string type). We are mostly inclined to implement 2) behaviour, but before proceed, I'd like to know what other people think about this. Thanks, -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-" ------------------------------------------------------------------------- This SF.net email is sponsored by: Microsoft Defy all challenges. Microsoft(R) Visual Studio 2008. http://clk.atdmt.com/MRT/go/vse0120000070mrt/direct/01/ _______________________________________________ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
