Hello all, I have been trying to find a package/addon that will provide a sparse array class to NumPy, or will at least trick NumPy to use a sparse array as a regular array, to no avail. By sparse array here, I donot mean a sparse matrix equation solver, but an array class that accepts a "default value". In other words, I would like to instantiate a 1000x1000x1000 (1e9) array that will have at most 5-10% populated (i.e. non-zero) elements. The current NumPy will instantiate the entire 1e9 array, which is a non-starter if you would like to calculate an expression with say 4-5 arrays. Instead, I'd like a class that will only store the populated cells, and return the default value for the others (ideally, but doing some smart disk I/O to preserve memory). I've tried SciPy, Scientific Python, and a few other modules floating around; none seem to do the trick, yet I can't help but wonder that this is not un uncommon setup for a lot of problem domains. Is there a package out there? If there isn't, where should I start looking to create one? From their description I think SparseLib++ at least would be a good starting point as a base library. As a secondary issue, is anyone aware of a package that can handle storage of such arrays? netCDF and HDF do not seem to fit the bill; a B-Tree library seems a more natural fit... Thanks in advance --any and all input appreciated, Costas