http://www.mail-archive.com/numpy-discussion@scipy.org/msg17595.html
Prompted by the thread above, I decided to see what it would take to implement ufuncs with masking in C. I described the result here: http://www.mail-archive.com/numpy-discussion@scipy.org/msg17698.html Now I am starting a new thread. The present state of the work is now in github: http://github.com/efiring/numpy-work/tree/cfastma I don't want to do any more until I have gotten some feedback from core developers. (And I would be delighted if someone wants to help with this, or take it over.) 1) The strategy I have started with is to make a full set of masked ufuncs alongside the existing ones, appending "_m" to their names. Only the binary ufuncs are implemented now, but the unary ufuncs can be handled similarly. Example: multiply(x, y, out) # present ufunc: no change multiply_m(x, y, mask, out) # new Where mask is True, the operation is skipped. 2) I have in mind the possibility of supporting two input masks and one output mask for binary operations. This would look like: multiply_mm(x, y, maskx, masky, out, outmask) outmask would be the logical_or of maskx and masky, and in the case of domained operations it would also be True where the arguments are outside the domain. This form would provide the fastest support for masked arrays, but would also take quite a bit more work, and would expand the namespace even more. I'm not sure it's worth it. 3) I have not yet taken any steps to modify numpy.ma to take advantage of the new ufuncs, but I think that will be quite simple. 4) Likewise, to save time, I am now just borrowing the regular ufunc docstrings. 5) No tests yet, Stefan. They can be added as soon as there is agreement on API and general strategy. 6) The present implementation is based on conceptually small modifications of the existing numpy code generation system. It required a lot of cut and paste, and yields a lot of nearly duplicated code. There may be better ways to do it--especially if it turns out it needs to be redone in some modified form. Eric _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
