On Tue, 2013-01-22 at 13:44 +0100, Sebastian Berg wrote: > Hey, > > On Tue, 2013-01-22 at 10:21 +0100, Todd wrote: > > I am trying to create a subclass of ndarray that has additional > > attributes. These attributes are maintained with most numpy functions > > if __array_finalize__ is used. > > > You can cover a bit more if you also implement `__array_wrap__`, though > unless you want to do something fancy, that just replaces the > `__array_finalize__` for the most part. But some (very few) functions > currently call `__array_wrap__` explicitly. >
Actually have to correct myself here. The default __array_wrap__ causes __array_finalize__ to be called as you would expect, so there is no need to use it unless you want to do something fancy. > > The main exception I have found is concatenate (and hstack/vstack, > > which just wrap concatenate). In this case, __array_finalize__ is > > passed an array that has already been stripped of the additional > > attributes, and I don't see a way to recover this information. > > > There are quite a few functions that simply do not preserve subclasses > (though I think more could/should call `__array_wrap__` probably, even > if the documentation may say that it is about ufuncs, there are some > example of this already). > `np.concatenate` is one of these. It always returns a base array. In any > case it gets a bit difficult if you have multiple input arrays (which > may not matter for you). > > > In my particular case at least, there are clear ways to handle corner > > cases (like being passed a class that lacks these attributes), so in > > principle there no problem handling concatenate in a general way, > > assuming I can get access to the attributes. > > > > > > So is there any way to subclass ndarray in such a way that concatenate > > can be handled properly? > > > Quite simply, no. If you compare masked arrays, they also provide their > own concatenate for this reason. > > I hope that helps a bit... > > Regards, > > Sebastian > > > I have been looking extensively online, but have not been able to find > > a clear answer on how to do this, or if there even is a way. > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
