It's funny you should mention this. One of the things I realized after SciPy this year is that there is a lot of interfacing of Numpy with C++ (as opposed to C) that could really benefit from a standard C++ wrapper around Numpy. A bunch of different projects all had home-grown, semi-complete solutions. A standard wrapper would be good for all of the reasons you mention (basically resource management), but also to have a cleaner syntax for accessing the elements that would abstract away striding, ordering etc. It may actually be possible to build it around boost::multiarray (the closest thing the upcoming C++-0x standard has to a n-dimensional array). Can't imagine I'll find time for it anytime soon, but it would be fun to work on... Cheers, Mike Christopher Barker wrote: > Michael Droettboom wrote: > >> I think we want to use std::vector where possible for new code, and >> convert as we go through old code. Manual malloc/free is just too error >> prone. (We're forced to do some by interfacing with Python, but it >> should be kept to a minimum). >> > > My understanding is that this is exactly the issue -- with std::vector > and friends you can't both get the data pointer and create a vector with > a data pointer, which would be what you'd want to do to interface > efficiently with numpy arrays. > > I've been looking for a way to address this problem, but my C++ is > pretty week. maybe smart pointers and/or other boost classes could help. > > > I do have an idea, though. The goal is a set of classes for working with > data that are convenient to use with pure C++, but also play with numpy > arrays. > > I imagine a simple vector/array set of classes build on top of a simple > object: a reference counted data block. This data block object would > simply hold a pointer to a block of data, maybe know how large it is, > maybe know what type the data is, and hold a reference count. when that > count drops to zero it deletes itself. > > The vector/array classes built on top of it would use the data block to > store their data, and increment/decrement the reference count as need > be. This would allow them to share data, have one array that is a subset > of another, using the same data block, etc. > > It should be easy to build numpy arrays from a system like this. > > Ideally (but I'm not sure how), the data blocks reference counting > system could be integrated with Python's, so that when a numpy array was > constructed from one, its reference count would be managed by python as > well as your C++ code. > > But maybe that's all just too much work... > > > -Chris > > > >