Mark Wiebe <mwwi...@gmail.com>: > With a non-boolean alpha mask, there's an implication of a > multiplication operator in there somewhere, but with a boolean mask, > the data can be any data whatsoever that doesn't necessarily support > any kind of blending operations.
My goal in raising the point is to find a common core that supports everything. The benefit of the np.ma module is that you have traditional numerical routines like median() and mean() that now sensibly handle missing data, plus a data structure (the paired array and mask) that you can use for other things of your own devising. All that has to happen is to allow the sense of the mask to be FALSE = the data are bad, TRUE = the data are good, and allow (not require) the mask to be of any numerical type, or at least of integer type as well as boolean. I believe that with these two basic requirements, everyone's needs can be met. Note that you could still have boolean masks, and could still have the bad=TRUE, good=FALSE of the current np.ma module, if you had a flag to set in the dtype for what sense of the mask you wanted. It could default to the current behavior if that makes people happy/breaks the least code. > For the image accumulation you're describing, I would use either a > structured array with 'color' and 'weight' fields, or have the last > element of the color channel be the weight (like an RGBA image) so > adding multiple weighted images together would add both the colors > and the weights simultaneously, without requiring a ufunc extension > supporting struct dtypes. Well, yes, we can always design a new data structure that meets our needs, and write all the routines that will ever operate on them. But we don't want that. We want to add a feature to the *old* data structure (i.e., a numerical array of the basic data) that makes the standard routines handle missing data sensibly so we don't have to rewrite them to do so. --jh-- _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
